def _what_to_break(cls, board, my_position, blast_strength):
x, y = my_position
to_break = list()
# To up
for dx in range(1, blast_strength):
if x + dx >= len(board[0]):
break
position = (x + dx, y)
if utility.position_is_rigid(board, position):
# stop searching this direction
break
elif utility.position_is_wood(
board, position) or utility.position_is_agent(
board, position):
to_break.append(constants.Item(board[position]))
break
# To down
for dx in range(1, blast_strength):
if x - dx < 0:
break
position = (x - dx, y)
if utility.position_is_rigid(board, position):
# stop searching this direction
break
elif utility.position_is_wood(
board, position) or utility.position_is_agent(
board, position):
to_break.append(constants.Item(board[position]))
break
# To right
for dy in range(1, blast_strength):
if y + dy >= len(board):
break
position = (x, y + dy)
if utility.position_is_rigid(board, position):
# stop searching this direction
break
elif utility.position_is_wood(
board, position) or utility.position_is_agent(
board, position):
to_break.append(constants.Item(board[position]))
break
# To left
for dy in range(1, blast_strength):
if y - dy < 0:
break
position = (x, y - dy)
if utility.position_is_rigid(board, position):
# stop searching this direction
break
elif utility.position_is_wood(
board, position) or utility.position_is_agent(
board, position):
to_break.append(constants.Item(board[position]))
break
return to_break
def act(self, obs, action_space):
# convert action_space
action_space = spaces.Discrete(action_space)
# convert obs
for key in ["board", "bomb_blast_strength", "bomb_life"]:
obs[key] = np.array(obs[key], dtype="uint8")
obs["position"] = tuple(obs["position"])
obs["teammate"] = constants.Item(obs["teammate"])
obs["enemies"] = [constants.Item(n) for n in obs["enemies"]]
return self._agent.act(obs, action_space)
def _djikstra(board, my_position, bombs, enemies, depth=None, exclude=None):
assert (depth is not None)
if exclude is None:
exclude = [
constants.Item.Fog, constants.Item.Rigid, constants.Item.Flames
]
def out_of_range(p_1, p_2):
'''Determines if two points are out of rang of each other'''
x_1, y_1 = p_1
x_2, y_2 = p_2
return abs(y_2 - y_1) + abs(x_2 - x_1) > depth
items = defaultdict(list)
dist = {}
prev = {}
Q = queue.PriorityQueue()
my_x, my_y = my_position
for r in range(max(0, my_x - depth), min(len(board), my_x + depth)):
for c in range(max(0, my_y - depth), min(len(board), my_y + depth)):
position = (r, c)
if any([
out_of_range(my_position, position),
utility.position_in_items(board, position, exclude),
]):
continue
if position == my_position:
dist[position] = 0
else:
dist[position] = np.inf
prev[position] = None
Q.put((dist[position], position))
for bomb in bombs:
if bomb['position'] == my_position:
items[constants.Item.Bomb].append(my_position)
while not Q.empty():
_, position = Q.get()
if utility.position_is_passable(board, position, enemies):
x, y = position
val = dist[(x, y)] + 1
for row, col in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
new_position = (row + x, col + y)
if new_position not in dist:
continue
if val < dist[new_position]:
dist[new_position] = val
prev[new_position] = position
item = constants.Item(board[position])
items[item].append(position)
return items, dist, prev
def move2wooden(obs):
res = [0] * 6
my_position = tuple(obs['position'])
board = np.array(obs['board'])
bombs = convert_bombs(np.array(obs['bomb_blast_strength']))
enemies = [consts.Item(e) for e in obs['enemies']]
items, dist, prev = _djikstra(board, my_position, bombs, enemies, depth=10)
direction = _near_wood(my_position, items, dist, prev, 2)
if direction:
res[direction.value] = 1
return res
def _filter_legal_actions(state):
my_position = tuple(state['position'])
board = np.array(state['board'])
enemies = [constants.Item(e) for e in state['enemies']]
ret = [constants.Action.Bomb]
for direction in directions:
position = utility.get_next_position(my_position, direction)
if utility.position_on_board(
board, position) and utility.position_is_passable(
board, position, enemies):
ret.append(direction)
return ret
def unsafe_directions(obs):
my_position = tuple(obs['position'])
board = np.array(obs['board'])
bombs = convert_bombs(np.array(obs['bomb_blast_strength']))
enemies = [consts.Item(e) for e in obs['enemies']]
items, dist, prev = _djikstra(board, my_position, bombs, enemies, depth=10)
unsafe_directions = _directions_in_range_of_bomb(board, my_position, bombs,
dist)
res = [0] * 6
for key in unsafe_directions:
if unsafe_directions[key] > 0:
res[key.value] = -1
return res
def wooden_wall(obs):
res = [0] * 6
my_position = tuple(obs['position'])
board = np.array(obs['board'])
bombs = convert_bombs(np.array(obs['bomb_blast_strength']))
enemies = [consts.Item(e) for e in obs['enemies']]
ammo = int(obs['ammo'])
blast_strength = int(obs['blast_strength'])
items, dist, prev = _djikstra(board, my_position, bombs, enemies, depth=10)
if _near_wood(my_position, items, dist, prev, 1):
if _maybe_bomb(ammo, blast_strength, items, dist, my_position):
res[-1] = 1
return res
def action(): #pylint: disable=W0612
'''handles an action over http'''
data = request.get_json()
observation = data.get("obs")
observation = json.loads(observation)
observation['teammate'] = constants.Item(observation['teammate'])
for enemy_id in range(len(observation['enemies'])):
observation['enemies'][enemy_id] = constants.Item(
observation['enemies'][enemy_id])
observation['position'] = tuple(observation['position'])
observation['board'] = np.array(observation['board'],
dtype=np.uint8)
observation['bomb_life'] = np.array(observation['bomb_life'],
dtype=np.float64)
observation['bomb_blast_strength'] = np.array(
observation['bomb_blast_strength'], dtype=np.float64)
action_space = data.get("action_space")
action_space = json.loads(action_space)
action = self.act(observation, action_space)
return jsonify({"action": action})
def kill_enemy(obs):
my_position = tuple(obs['position'])
board = np.array(obs['board'])
bombs = convert_bombs(np.array(obs['bomb_blast_strength']))
enemies = [consts.Item(e) for e in obs['enemies']]
ammo = int(obs['ammo'])
blast_strength = int(obs['blast_strength'])
items, dist, prev = _djikstra(board, my_position, bombs, enemies, depth=10)
res = [0] * 6
if _is_adjacent_enemy(items, dist, enemies) and _maybe_bomb(
ammo, blast_strength, items, dist, my_position):
res[-1] = 1
return res
def __init__(self, obs, init=False, bombing_agents={}, board_size=11):
self._game_mode = constants.GameType.FFA
self.move = None
self._board_size = board_size
self._obs = obs
self._my_position = tuple(obs['position'])
self._board = np.array(obs['board'])
self._bomb_life = np.array(self._obs['bomb_life'])
self._teammate = obs['teammate']
self._enemies = [constants.Item(e) for e in obs['enemies']]
self._ammo = int(obs['ammo'])
self.fm = forward_model.ForwardModel()
self.self_agent = self.find_self_agent(self._obs)
agents_id = [
constants.Item.Agent0, constants.Item.Agent1, constants.Item.Agent2,
constants.Item.Agent3
]
self._agents = [
characters.Bomber(aid.value, "FFA") for aid in agents_id
] # remember to modifiy if it is team or radio mode
self.bombing_agents = copy.deepcopy(bombing_agents)
self.score = 0
if init:
self.curr_flames = self.convert_flames(
self._board) # determine by confirming the map
self.curr_bombs = self.convert_bombs(
np.array(obs['bomb_blast_strength']),
np.array(obs['bomb_life']))
self.curr_items = self.convert_items(self._board)
self.curr_agents = self.convert_agents(self._board)
self.last_items = self.curr_items
if (bombing_agents != {}):
self.curr_bombs = self.convert_bombs_two(
np.array(self._obs['bomb_blast_strength']), self._bomb_life,
bombing_agents)
def find_next_move(self, obs, action_space, win_condition, score_func,
bombing_agents):
self.action_space = action_space
self.win_condition = win_condition
self.bombing_agents = bombing_agents
self.score_func = score_func
self.bombing_agents = bombing_agents
my_pos = tuple(obs['position'])
board = np.array(obs['board'])
self.board = np.array(obs['board'])
self._enemies = [constants.Item(e) for e in obs['enemies']]
tree = gn.Tree(obs, True, self.bombing_agents)
#get the root node
self.rootNode = tree.get_root_node()
#need way to find terminating condition
self.end_time = 30
start_time = time.time()
elapsed = 0
self.bfs(self.rootNode, start_time)
# max_score = self.score_func(self.rootNode.get_child_with_max_score().state.obs)
max_score = -1
winner_node = None
for child in self.rootNode.childArray:
if (child.score) > max_score:
max_score = child.score
winner_node = child
# print("max score {0} reached level {1} with move {2}".format(max_score, endLevel, winner_node.state.move))
if winner_node is None:
return constants.Action.Stop.value
return (winner_node.state.move)
def reward(self, id, current_obs, info):
reward = 0
if utility._position_is_item(self.prev_obs[id]['board'],
current_obs[id]['position'],
constants.Item.IncrRange):
reward += 0.01
self.stat[id][Metrics.IncrRange.name] += 1
if utility._position_is_item(self.prev_obs[id]['board'],
current_obs[id]['position'],
constants.Item.ExtraBomb):
reward += 0.01
self.stat[id][Metrics.ExtraBomb.name] += 1
if utility._position_is_item(
self.prev_obs[id]['board'], current_obs[id]['position'],
constants.Item.Kick) and not self.prev_obs[id]['can_kick']:
reward += 0.02
self.stat[id][Metrics.Kick.name] = True
for i in range(10, 14):
if i in self.alive_agents and i not in current_obs[id]['alive']:
if constants.Item(value=i) in current_obs[id]['enemies']:
reward += 0.5
self.stat[id][Metrics.EnemyDeath.name] += 1
elif i - 10 == id:
reward += -1
self.stat[id][Metrics.DeadOrSuicide.name] += 1
else:
reward += -0.5
if info['result'] == constants.Result.Tie:
reward += -1
return reward
def _find_reachable_items(self, list_boards, my_position, time_positions):
"""
Find items reachable from my position
Parameters
----------
list_boards : list
list of boards, generated by _board_sequence
my_position : tuple
my position, where the search starts
time_positions : list
survivable time-positions, generated by _search_time_expanded_network
Return
------
items : dict
items[item] : list of time-positions from which one can reach item
reached : array
minimum time to reach each position on the board
next_to_items : dict
next_to_items[item] : list of time-positions from which one can reach
the position next to item
"""
# items found on time_positions and the boundary (for Wood)
items = defaultdict(list)
# reached[position] : minimum time to reach the position
reached = np.full(self.board_shape, np.inf)
# whether already checked the position
_checked = np.full(self.board_shape, False)
# positions next to wood or other agents (count twice if next to two woods)
next_to_items = defaultdict(list)
for t, positions in enumerate(time_positions):
# check the positions reached at time t
board = list_boards[t]
for position in positions:
if reached[position] < np.inf:
continue
reached[position] = t
item = constants.Item(board[position])
items[item].append((t,) + position)
_checked[position] = True
x, y = position
for row, col in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
next_position = (x + row, y + col)
if not self._on_board(next_position):
continue
if _checked[next_position]:
continue
_checked[next_position] = True
if utility.position_is_agent(board, next_position):
item = constants.Item(board[next_position])
items[item].append((t,)+next_position)
next_to_items[item].append((t,) + position)
# ignoring wall that will not exist when explode
if utility.position_is_wood(list_boards[-1], next_position):
item = constants.Item(board[next_position])
items[item].append((t,)+next_position)
next_to_items[item].append((t,) + position)
return items, reached, next_to_items
def act(self, obs, action_space, info):
#
# Definitions
#
#board = obs['board']
board = info["recently_seen"]
my_position = obs["position"] # tuple([x,y]): my position
my_ammo = obs['ammo'] # int: the number of bombs I have
my_blast_strength = obs['blast_strength']
my_enemies = [
constants.Item(e) for e in obs['enemies']
if e != constants.Item.AgentDummy
]
if obs["teammate"] != constants.Item.AgentDummy:
my_teammate = obs["teammate"]
else:
my_teammate = None
my_kick = obs["can_kick"] # whether I can kick
#
# Understand current situation
#
# positions that might be blocked
if info["teammate_position"] is None:
agent_positions = info["enemy_positions"]
else:
agent_positions = info["enemy_positions"] + [
info["teammate_position"]
]
# survivable actions
if len(info["enemy_positions"]) > 0:
mobility = self._enemy_mobility
else:
mobility = 0
n_survivable, is_survivable, list_boards \
= self._get_survivable(obs, info, my_position, info["my_next_position"], agent_positions,
info["all_kickable"], allow_kick_to_fog=False,
enemy_mobility=mobility, enemy_bomb=self._enemy_bomb,
step_to_collapse=info["step_to_collapse"],
collapse_ring=info["collapse_ring"])
for a in info["might_block_actions"]:
n_survivable[a] = np.zeros(self._search_range)
is_survivable[a] = False
survivable_actions = list()
for a in is_survivable:
if not is_survivable[a]:
continue
if info["might_blocked"][a] and not is_survivable[
constants.Action.Stop]:
continue
if n_survivable[a][-1] <= 1:
is_survivable[a] = False
continue
survivable_actions.append(a)
#
# Choose action
#
if len(survivable_actions) == 0:
#
# return None, if no survivable actions
#
return None
elif len(survivable_actions) == 1:
#
# Choose the survivable action, if it is the only choice
#
action = survivable_actions[0]
return action.value
if all([
info["prev_action"]
not in [constants.Action.Stop, constants.Action.Bomb],
info["prev_position"] == my_position
]):
# if previously blocked, do not reapeat with some probability
self._inv_tmp *= self._backoff
else:
self._inv_tmp = self._inv_tmp_init
#
# Bomb at a target
#
# fraction of blocked node in the survival trees of enemies
_list_boards = info["list_boards_no_move"]
if obs["bomb_blast_strength"][my_position]:
for b in _list_boards:
if utility.position_is_agent(b, my_position):
b[my_position] = constants.Item.Bomb.value
else:
for b in _list_boards:
if utility.position_is_agent(b, my_position):
b[my_position] = constants.Item.Passage.value
total_frac_blocked, n_survivable_nodes, blocked_time_positions \
= self._get_frac_blocked(_list_boards, my_enemies, board, obs["bomb_life"])
if info["teammate_position"] is not None:
total_frac_blocked_teammate, n_survivable_nodes_teammate, blocked_time_positions_teammate \
= self._get_frac_blocked(_list_boards, [my_teammate], board, obs["bomb_life"])
if n_survivable_nodes_teammate[my_teammate] > 0:
LB = self._teammate_survivability_threshold / n_survivable_nodes_teammate[
my_teammate]
positions_teammate_safe = np.where(
total_frac_blocked_teammate < LB)
total_frac_blocked_teammate[positions_teammate_safe] = 0
p_survivable = defaultdict(float)
for action in n_survivable:
p_survivable[action] = sum(
n_survivable[action]) / self._my_survivability_threshold
if p_survivable[action] > 1:
p_survivable[action] = 1
block = defaultdict(float)
for action in [
constants.Action.Stop, constants.Action.Up,
constants.Action.Down, constants.Action.Left,
constants.Action.Right
]:
next_position = info["my_next_position"][action]
if next_position is None:
continue
if next_position in info["all_kickable"]:
# kick will be considered later
continue
if all([
utility.position_is_flames(board, next_position),
info["flame_life"][next_position] > 1,
is_survivable[constants.Action.Stop]
]):
# if the next position is flames,
# I want to stop to wait, which must be feasible
block[action] = total_frac_blocked[
next_position] * p_survivable[constants.Action.Stop]
if info["teammate_position"] is not None:
block[action] *= (
1 - total_frac_blocked_teammate[next_position])
if block[action] > 0:
block[action] *= self._inv_tmp
block[action] -= np.log(-np.log(self.random.uniform()))
continue
elif not is_survivable[action]:
continue
if all([
info["might_blocked"][action],
not is_survivable[constants.Action.Stop]
]):
continue
block[action] = total_frac_blocked[next_position] * p_survivable[
action]
if info["teammate_position"] is not None:
block[action] *= (1 -
total_frac_blocked_teammate[next_position])
if block[action] > 0:
block[action] *= self._inv_tmp
block[action] -= np.log(-np.log(self.random.uniform()))
if info["might_blocked"][action]:
block[action] = (total_frac_blocked[my_position] *
p_survivable[constants.Action.Stop] +
total_frac_blocked[next_position] *
p_survivable[action]) / 2
if info["teammate_position"] is not None:
block[action] *= (
1 - total_frac_blocked_teammate[next_position])
if block[action] > 0:
block[action] *= self._inv_tmp
block[action] -= np.log(-np.log(self.random.uniform()))
if is_survivable[constants.Action.Bomb]:
list_boards_with_bomb, _ \
= self._board_sequence(board,
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
my_blast_strength=my_blast_strength,
my_action=constants.Action.Bomb,
step_to_collapse=info["step_to_collapse"],
collapse_ring=info["collapse_ring"])
n_survivable_nodes_with_bomb = defaultdict(int)
for enemy_position in info["enemy_positions"]:
# get survivable tree of the enemy
_survivable = search_time_expanded_network(
list_boards_with_bomb, enemy_position)
n_survivable_nodes_with_bomb[enemy_position] = sum(
[len(positions) for positions in _survivable])
n_with_bomb = sum([
n_survivable_nodes_with_bomb[enemy_position]
for enemy_position in info["enemy_positions"]
])
n_with_none = sum(
[n_survivable_nodes[enemy] for enemy in my_enemies])
if n_with_none == 0:
total_frac_blocked_with_bomb = 0
# place more bombs, so the stacked enemy cannot kick
x, y = my_position
for dx, dy in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
next_position = (x + dx, y + dy)
following_position = (x + 2 * dx, y + 2 * dy)
if not self._on_board(following_position):
continue
if all([
obs["bomb_life"][next_position] > 0,
board[following_position] >
constants.Item.AgentDummy.value
]):
total_frac_blocked_with_bomb = 1
else:
total_frac_blocked_with_bomb = 1 - n_with_bomb / n_with_none
action = constants.Action.Bomb
block[action] = total_frac_blocked_with_bomb
# block[action] += total_frac_blocked[my_position] * (eisenachAgents - total_frac_blocked_with_bomb)
block[action] *= p_survivable[action]
block_teammate_with_bomb = None
if block[action] > 0:
if info["teammate_position"] is not None:
block_teammate_with_bomb \
= self._get_frac_blocked_two_lists(list_boards_with_bomb,
n_survivable_nodes_teammate,
board,
[my_teammate],
ignore_dying_agent=True)
block_teammate_with_bomb \
+= total_frac_blocked_teammate[my_position] * (1 - block_teammate_with_bomb)
block[action] *= (1 - block_teammate_with_bomb)
block[action] *= self._inv_tmp
block[action] -= np.log(-np.log(self.random.uniform()))
for next_position in info["kickable"]:
action = self._get_direction(my_position, next_position)
if not is_survivable[action]:
continue
list_boards_with_kick, _ \
= self._board_sequence(board,
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
my_action=action,
can_kick=True,
step_to_collapse=info["step_to_collapse"],
collapse_ring=info["collapse_ring"])
block[action] \
= self._get_frac_blocked_two_lists(list_boards_with_kick,
n_survivable_nodes,
board,
my_enemies,
ignore_dying_agent=True)
block[action] += total_frac_blocked[next_position] * (
1 - block[action])
block[action] *= p_survivable[action]
if block[action] > 0:
if info["teammate_position"] is not None:
block_teammate_with_kick \
= self._get_frac_blocked_two_lists(list_boards_with_kick,
n_survivable_nodes_teammate,
board, [my_teammate],
ignore_dying_agent=True)
block_teammate_with_kick \
+= total_frac_blocked_teammate[next_position] * (1 - block_teammate_with_kick)
block[action] *= (1 - block_teammate_with_kick)
block[action] *= self._inv_tmp
block[action] -= np.log(-np.log(self.random.uniform()))
max_block = 0 # do not choose zero blocking action as the best
best_action = None
for action in block:
if block[action] > max_block:
max_block = block[action]
best_action = action
if block[constants.Action.Bomb] > self._bomb_threshold * self._inv_tmp:
if info["teammate_position"] is not None:
if block_teammate_with_bomb is None:
block_teammate_with_bomb \
= self._get_frac_blocked_two_lists(list_boards_with_bomb,
n_survivable_nodes_teammate,
board,
[my_teammate],
ignore_dying_agent=True)
teammate_safety = block_teammate_with_bomb * n_survivable_nodes_teammate[
my_teammate]
if any([
teammate_safety >
self._teammate_survivability_threshold,
block_teammate_with_bomb < self._interfere_threshold,
block_teammate_with_bomb <
total_frac_blocked_teammate[my_position]
]):
teammate_ok = True
else:
teammate_ok = False
else:
teammate_ok = True
if teammate_ok:
if best_action == constants.Action.Bomb:
return constants.Action.Bomb.value
if best_action == constants.Action.Stop:
return constants.Action.Bomb.value
#
# Move towards where to bomb
#
if best_action not in [None, constants.Action.Bomb]:
next_position = info["my_next_position"][best_action]
should_chase = (total_frac_blocked[next_position] >
self._chase_threshold)
if info["teammate_position"] is not None:
teammate_safety = total_frac_blocked_teammate[
next_position] * n_survivable_nodes_teammate[my_teammate]
if any([
teammate_safety >
self._teammate_survivability_threshold,
total_frac_blocked_teammate[next_position] <
self._interfere_threshold,
total_frac_blocked_teammate[next_position] <
total_frac_blocked_teammate[my_position]
]):
teammate_ok = True
else:
teammate_ok = False
else:
teammate_ok = True
if should_chase and teammate_ok:
if all([
utility.position_is_flames(board, next_position),
info["flame_life"][next_position] > 1,
is_survivable[constants.Action.Stop]
]):
action = constants.Action.Stop
return action.value
else:
return best_action.value
# Exclude the action representing stop to wait
max_block = 0 # do not choose zero blocking action as the best
best_action = None
for action in survivable_actions:
if block[action] > max_block:
max_block = block[action]
best_action = action
#
# Do not take risky actions when not interacting with enemies
#
most_survivable_action = self._action_most_survivable(n_survivable)
if total_frac_blocked[my_position] > 0:
# ignore actions with low survivability
_survivable_actions = list()
for action in n_survivable:
n = sum(n_survivable[action])
if not is_survivable[action]:
continue
elif n > self._my_survivability_threshold:
_survivable_actions.append(action)
else:
is_survivable[action] = False
if len(_survivable_actions) > 1:
survivable_actions = _survivable_actions
elif best_action is not None:
return best_action.value
else:
# Take the most survivable action
return most_survivable_action.value
#
# Do not interfere with teammate
#
if all([
info["teammate_position"] is not None,
len(info["enemy_positions"]) > 0 or len(info["curr_bombs"]) > 0
]):
# ignore actions that interfere with teammate
min_interfere = np.inf
least_interfere_action = None
_survivable_actions = list()
for action in survivable_actions:
if action == constants.Action.Bomb:
"""
if block_teammate_with_bomb is None:
block_teammate_with_bomb \
= self._get_frac_blocked_two_lists(list_boards_with_bomb,
n_survivable_nodes_teammate,
board,
[my_teammate],
ignore_dying_agent=True)
frac = block_teammate_with_bomb
"""
continue
else:
next_position = info["my_next_position"][action]
frac = total_frac_blocked_teammate[next_position]
if frac < min_interfere:
min_interfere = frac
least_interfere_action = action
if frac < self._interfere_threshold:
_survivable_actions.append(action)
else:
is_survivable[action] = False
if len(_survivable_actions) > 1:
survivable_actions = _survivable_actions
elif best_action is not None:
# Take the least interfering action
return best_action.value
else:
return least_interfere_action.value
consider_bomb = True
if not is_survivable[constants.Action.Bomb]:
consider_bomb = False
#
# Find reachable items
#
# List of boards simulated
list_boards, _ = self._board_sequence(
board,
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
enemy_mobility=mobility,
enemy_bomb=self._enemy_bomb,
enemy_positions=agent_positions,
agent_blast_strength=info["agent_blast_strength"],
step_to_collapse=info["step_to_collapse"],
collapse_ring=info["collapse_ring"])
# some bombs may explode with extra bombs, leading to under estimation
for t in range(len(list_boards)):
flame_positions = np.where(
info["list_boards_no_move"][t] == constants.Item.Flames.value)
list_boards[t][flame_positions] = constants.Item.Flames.value
# List of the set of survivable time-positions at each time
# and preceding positions
survivable, prev, _, _ = self._search_time_expanded_network(
list_boards, my_position)
if len(survivable[-1]) == 0:
survivable = [set() for _ in range(len(survivable))]
# Items and bomb target that can be reached in a survivable manner
if "escape" in info:
reachable_items, _, next_to_items \
= self._find_reachable_items(list_boards,
my_position,
survivable,
might_powerup=info["escape"]) # might_powerup is the escape from collapse
else:
_, _, next_to_items \
= self._find_reachable_items(list_boards,
my_position,
survivable)
#
# If I have seen an enemy recently and cannot see him now, them move to the last seen position
#
action = self._action_to_enemy(my_position,
next_to_items[constants.Item.Fog], prev,
is_survivable, info)
if action is not None:
return action.value
#
# If I have seen a teammate recently, them move away from the last seen position
#
action = self._action_away_from_teammate(
my_position, next_to_items[constants.Item.Fog], prev,
is_survivable, info)
if action is not None:
return action.value
#
# Move to the places that will not be collapsed
#
if "escape" in info:
# might_powerup is the escape from collapse
action = self._action_to_might_powerup(my_position,
reachable_items, prev,
is_survivable)
if action is not None:
print("Escape from collapse", action)
return action.value
#
# Move towards a fog where we have not seen longest
#
action = self._action_to_fog(my_position,
next_to_items[constants.Item.Fog], prev,
is_survivable, info)
if action is not None:
#if True:
if self.random.uniform() < 0.8:
return action.value
#
# Choose most survivable action
#
max_block = 0
best_action = None
for action in survivable_actions:
if action == constants.Action.Bomb:
continue
score = block[action]
if action != constants.Action.Bomb:
score += np.random.uniform(0, 1e-3)
if score > max_block:
max_block = score
best_action = action
if best_action is None:
max_p = 0
best_action = None
for action in p_survivable:
score = p_survivable[action]
if action != constants.Action.Bomb:
score += np.random.uniform(0, 1e-3)
if score > max_p:
max_p = score
best_action = action
if best_action is None:
# this should not be the case
return None
else:
return best_action.value
def _find_safe_directions(self, board, my_position, unsafe_directions,
bombs, enemies, item):
def is_stuck_direction(next_position, bomb_range, next_board, enemies):
'''Helper function to do determine if the agents next move is possible.'''
Q = queue.PriorityQueue()
Q.put((0, next_position))
seen = set()
next_x, next_y = next_position
is_stuck = True
while not Q.empty():
dist, position = Q.get()
seen.add(position)
#FIXME is_stuck=False
position_x, position_y = position
if next_x != position_x and next_y != position_y:
is_stuck = False
break
if dist > bomb_range:
is_stuck = False
break
for row, col in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
new_position = (row + position_x, col + position_y)
if new_position in seen:
continue
if not utility.position_on_board(next_board, new_position):
continue
if not utility.position_is_passable(
next_board, new_position, enemies):
continue
dist = abs(row + position_x -
next_x) + abs(col + position_y - next_y)
Q.put((dist, new_position))
return is_stuck
# All directions are unsafe. Return a position that won't leave us locked.
safe = []
if len(unsafe_directions) == 4:
next_board = board.copy()
next_board[my_position] = constants.Item.Bomb.value
disallowed = []
for direction, bomb_range in unsafe_directions.items():
next_position = utility.get_next_position(
my_position, direction)
next_x, next_y = next_position
if not utility.position_on_board(next_board, next_position) or \
not utility.position_is_passable(next_board, next_position, enemies):
disallowed.append(direction)
continue
if not is_stuck_direction(next_position, bomb_range,
next_board, enemies):
# We found a direction that works. The .items provided
# a small bit of randomness. So let's go with this one.
return [direction]
if not safe:
#当决定不动之前，判断是否是原地放炸弹,如果是原地放炸弹那么从unsafe_directions中随机一个
# for i in bombs:
# if len(bombs) == 1 :
if len(item[constants.Item(3)]) == 1:
# if my_position == i['position']:
for bomb in bombs:
if my_position == bomb['position']:
safe = [
k for k in unsafe_directions
if k not in disallowed
]
# break
if not safe:
safe = [constants.Action.Stop]
return safe
x, y = my_position
disallowed = [] # The directions that will go off the board.
for row, col in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
position = (x + row, y + col)
direction = utility.get_direction(my_position, position)
# Don't include any direction that will go off of the board.
if not utility.position_on_board(board, position):
disallowed.append(direction)
continue
# Don't include any direction that we know is unsafe.
if direction in unsafe_directions:
#当这个不安全位置不能通过的时候就disallow，防止踢炸弹
if not utility.position_is_passable(board, position, enemies):
disallowed.append(direction)
#当往不安全方向走，正好被炸死的话，那么就不能走。（刚好被炸死需要通过life来制定）
# if
continue
if utility.position_is_passable(
board, position, enemies) or utility.position_is_fog(
board, position):
#可能移动一个位置，隔壁存在炸弹
safe.append(direction)
for bomb in bombs:
if bomb['bomb_life'] == 1:
bomb_x, bomb_y = bomb['position']
if bomb_x == position[0] and abs(
bomb_y -
position[1]) <= bomb['blast_strength']:
#remove the direction
safe.pop()
break
elif bomb_y == position[1] and abs(
bomb_x -
position[0]) <= bomb['blast_strength']:
safe.pop()
break
if not safe:
# We don't have any safe directions, so return something that is allowed.
safe = [k for k in unsafe_directions if k not in disallowed]
if not safe:
# We don't have ANY directions. So return the stop choice.
return [constants.Action.Stop]
return safe
def act(self, obs, action_space):
t0 = time.perf_counter()
# The number of steps
self._steps += 1
# TODO: deepcopy are not needed with Docker
board = deepcopy(obs["board"])
bomb_life = deepcopy(obs["bomb_life"])
bomb_blast_strength = deepcopy(obs["bomb_blast_strength"])
my_position = obs["position"]
my_enemies = [constants.Item(e) for e in obs['enemies'] if e != constants.Item.AgentDummy]
if obs["teammate"] != constants.Item.AgentDummy:
my_teammate = obs["teammate"]
else:
my_teammate = None
teammate_position = None
if my_teammate is not None:
rows, cols = np.where(board==my_teammate.value)
if len(rows):
teammate_position = (rows[0], cols[0])
info = dict()
info["prev_action"] = self._prev_action
info["prev_position"] = self._prev_position
#
# Whether each location is Rigid
#
# False may be an unknown
#
if self._num_rigid_found < constants.NUM_RIGID:
self._is_rigid += (board == constants.Item.Rigid.value)
self._is_rigid += (board.T == constants.Item.Rigid.value)
self._num_rigid_found = np.sum(self._is_rigid)
self._rigid_locations = np.where(self._is_rigid)
self._unreachable = ~self._get_reachable(self._is_rigid)
self._unreachable_locations = np.where(self._unreachable)
#
# What we have seen last time, and how many steps have past since then
#
# Once we see a Rigid, we always see it
#
visible_locations = np.where(board != constants.Item.Fog.value)
self._last_seen[visible_locations] = board[visible_locations]
#self._last_seen[self._rigid_locations] = constants.Item.Rigid.value
# unreachable -> rigid
self._last_seen[self._unreachable_locations] = constants.Item.Rigid.value
self._since_last_seen += 1
self._since_last_seen[visible_locations] = 0
self._since_last_seen[np.where(self._is_rigid)] = 0
if self._steps == 0:
# We have some knowledge about the initial configuration of the board
C = constants.BOARD_SIZE - 2
self._last_seen[(1, 1)] = constants.Item.Agent0.value
self._last_seen[(C, 1)] = constants.Item.Agent1.value
self._last_seen[(C, C)] = constants.Item.Agent2.value
self._last_seen[(1, C)] = constants.Item.Agent3.value
rows = np.array([1, C, 1, C])
cols = np.array([1, 1, C, C])
self._since_last_seen[(rows, cols)] = 0
rows = np.array([1, 1, 1, 1, 2, 3, C - 1, C - 2, C, C, C, C, 2, 3, C - 1, C - 2])
cols = np.array([2, 3, C - 1, C - 2, 1, 1, 1, 1, 2, 3, C - 1, C - 2, C, C, C, C])
self._last_seen[(rows, cols)] = constants.Item.Passage.value
self._since_last_seen[(rows, cols)] = 0
info["since_last_seen"] = self._since_last_seen
info["last_seen"] = self._last_seen
#
# Modify the board
#
#fog_positions = np.where(board==constants.Item.Fog.value)
#board[fog_positions] = self._last_seen[fog_positions]
board[self._unreachable_locations] = constants.Item.Rigid.value
#
# Summarize information about bombs
#
# curr_bombs : list of current bombs
# moving_direction : array of moving direction of bombs
info["curr_bombs"], info["moving_direction"] \
= self._get_bombs(board,
bomb_blast_strength, self._prev_bomb_blast_strength,
bomb_life, self._prev_bomb_life)
self._prev_bomb_life = bomb_life.copy()
self._prev_bomb_blast_strength = bomb_blast_strength.copy()
#
# Bombs to be exploded in the next step
#
curr_bomb_position_strength = list()
rows, cols = np.where(bomb_blast_strength > 0)
for position in zip(rows, cols):
strength = int(bomb_blast_strength[position])
curr_bomb_position_strength.append((position, strength))
#
# Summarize information about flames
#
if self._prev_board is not None:
info["curr_flames"], self._prev_flame_life \
= self._get_flames(board,
self._prev_board[-1],
self._prev_flame_life,
self._prev_bomb_position_strength,
curr_bomb_position_strength,
self._prev_moving_direction)
else:
info["curr_flames"] = []
info["flame_life"] = self._prev_flame_life
self._prev_moving_direction = deepcopy(info["moving_direction"])
self._prev_bomb_position_strength = curr_bomb_position_strength
#
# List of simulated boards, assuming enemies stay unmoved
#
info["list_boards_no_move"], _ \
= self._board_sequence(board,
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
enemy_mobility=0)
#
# Might appear item from flames
#
info["might_powerup"] = np.full(self.board_shape, False)
if self._prev_board is None:
# Flame life is 2
# flame life is hardcoded in pommmerman/characters.py class Flame
self._prev_board = [deepcopy(board), deepcopy(board), deepcopy(board)]
else:
old_board = self._prev_board.pop(0)
self._prev_board.append(deepcopy(board))
if self._might_remaining_powerup:
# was wood and now flames
was_wood = (old_board == constants.Item.Wood.value)
now_flames = (board == constants.Item.Flames.value)
info["might_powerup"] = was_wood * now_flames
# now wood and will passage
now_wood = (board == constants.Item.Wood.value)
become_passage = (info["list_boards_no_move"][-1] ==constants.Item.Passage.value)
info["might_powerup"] += now_wood * become_passage
maybe_powerup = info["might_powerup"] \
+ (self._last_seen == constants.Item.Wood.value) \
+ (self._last_seen == constants.Item.ExtraBomb.value) \
+ (self._last_seen == constants.Item.IncrRange.value) \
+ (self._last_seen == constants.Item.Kick.value)
if not maybe_powerup.any():
self._might_remaining_powerup = False
# update the estimate of enemy blast strength
rows, cols = np.where(bomb_life == constants.DEFAULT_BOMB_LIFE - 1)
for position in zip(rows, cols):
if position == my_position:
continue
enemy = board[position]
self._agent_blast_strength[enemy] = bomb_blast_strength[position]
info["agent_blast_strength"] = self._agent_blast_strength
# update the last seen enemy position
for agent in self._since_last_seen_agent:
self._since_last_seen_agent[agent] += 1
for enemy in my_enemies:
rows, cols = np.where(board == enemy.value)
if len(rows):
self._last_seen_agent_position[enemy] = (rows[0], cols[0])
self._since_last_seen_agent[enemy] = 0
continue
if teammate_position is not None:
self._last_seen_agent_position[my_teammate] = teammate_position
self._since_last_seen_agent[my_teammate] = 0
info["last_seen_agent_position"] = self._last_seen_agent_position
info["since_last_seen_agent"] = self._since_last_seen_agent
#
# Choose a slave to act
#
if self._isolated:
is_wood_visible = (constants.Item.Wood.value in board)
is_closed = self._is_closed(board, my_position)
if any([not is_wood_visible, not is_closed]):
self._isolated = False
action = None
if self._isolated:
# Act with an agent who do not consider other agents
if verbose:
print("IsolatedAgent: ", end="")
action = self.isolated_slave.act(obs, action_space, info)
# elif not self._might_remaining_powerup:
# Act with an agent who do not consider powerups
# print("BattleAgent: ", end="")
# action = self.battle_slave.act(obs, action_space, info)
else:
if verbose:
print("GenericAgent: ", end="")
action = self.generic_slave.act(obs, action_space, info)
if action is None:
# Act with a special agent, who only seeks to survive
if verbose:
print("\nSurvivingAgent: ", end="")
action = self.surviving_slave.act(obs, action_space, info)
this_time = time.perf_counter() - t0
if this_time > self.max_time:
self.max_time = this_time
self._prev_action = action
self._prev_position = my_position
return action
def act(self, obs, action_space, info):
#
# Definitions
#
board = obs['board']
recently_seen_positions = (info["since_last_seen"] < 3)
board[recently_seen_positions] = info["last_seen"][recently_seen_positions]
my_position = obs["position"] # tuple([x,y]): my position
my_ammo = obs['ammo'] # int: the number of bombs I have
my_blast_strength = obs['blast_strength']
my_enemies = [constants.Item(e) for e in obs['enemies'] if e != constants.Item.AgentDummy]
if obs["teammate"] != constants.Item.AgentDummy:
my_teammate = obs["teammate"]
else:
my_teammate = None
my_kick = obs["can_kick"] # whether I can kick
if verbose:
print("my position", my_position, "ammo", my_ammo, "blast", my_blast_strength, "kick", my_kick, end="\t")
my_next_position = {constants.Action.Stop: my_position,
constants.Action.Bomb: my_position}
for action in [constants.Action.Up, constants.Action.Down,
constants.Action.Left, constants.Action.Right]:
next_position = self._get_next_position(my_position, action)
if self._on_board(next_position):
if board[next_position] == constants.Item.Rigid.value:
my_next_position[action] = None
else:
my_next_position[action] = next_position
else:
my_next_position[action] = None
#
# Understand current situation
#
if all([info["prev_action"] not in [constants.Action.Stop, constants.Action.Bomb],
info["prev_position"] == my_position]):
# if previously blocked, do not reapeat with some probability
self._inv_tmp *= self._backoff
else:
self._inv_tmp = self._inv_tmp_init
# enemy positions
enemy_positions = list()
for enemy in my_enemies:
rows, cols = np.where(board==enemy.value)
if len(rows) == 0:
continue
enemy_positions.append((rows[0], cols[0]))
# teammate position
teammate_position = None
if my_teammate is not None:
rows, cols = np.where(board==my_teammate.value)
if len(rows):
teammate_position = (rows[0], cols[0])
# Positions where we kick a bomb if we move to
if my_kick:
kickable, might_kickable = self._kickable_positions(obs, info["moving_direction"])
else:
kickable = set()
might_kickable = set()
# positions that might be blocked
if teammate_position is None:
agent_positions = enemy_positions
else:
agent_positions = enemy_positions + [teammate_position]
might_blocked = self._get_might_blocked(board, my_position, agent_positions, might_kickable)
# enemy positions over time
# these might be dissappeared due to extra flames
if len(enemy_positions):
rows = [p[0] for p in enemy_positions]
cols = [p[1] for p in enemy_positions]
list_enemy_positions = [(rows, cols)]
_enemy_positions = list()
for t in range(self._enemy_mobility):
rows, cols = list_enemy_positions[-1]
for x, y in zip(rows, cols):
for dx, dy in [(1, 0), (-1, 0), (0, 1), (0, -1)]:
next_position = (x + dx, y + dy)
if not self._on_board(next_position):
continue
_board = info["list_boards_no_move"][t]
if utility.position_is_passage(_board, next_position):
_enemy_positions.append(next_position)
_enemy_positions = set(_enemy_positions)
rows = [p[0] for p in _enemy_positions]
cols = [p[1] for p in _enemy_positions]
list_enemy_positions.append((rows, cols))
else:
list_enemy_positions = []
# survivable actions
is_survivable = dict()
for a in self._get_all_actions():
is_survivable[a] = False
n_survivable = dict()
list_boards = dict()
for my_action in self._get_all_actions():
next_position = my_next_position[my_action]
if next_position is None:
continue
if my_action == constants.Action.Bomb:
if any([my_ammo == 0,
obs["bomb_blast_strength"][next_position] > 0]):
continue
if all([utility.position_is_flames(board, next_position),
info["flame_life"][next_position] > 1]):
continue
if all([my_action != constants.Action.Stop,
obs["bomb_blast_strength"][next_position] > 0,
next_position not in set.union(kickable, might_kickable)]):
continue
if next_position in set.union(kickable, might_kickable):
# do not kick into fog
dx = next_position[0] - my_position[0]
dy = next_position[1] - my_position[1]
position = next_position
is_fog = False
while self._on_board(position):
if utility.position_is_fog(board, position):
is_fog = True
break
position = (position[0] + dx, position[1] + dy)
if is_fog:
continue
# list of boards from next steps
list_boards[my_action], _ \
= self._board_sequence(obs["board"],
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
my_blast_strength=my_blast_strength,
my_action=my_action,
can_kick=my_kick,
enemy_mobility=self._enemy_mobility,
enemy_bomb=self._enemy_bomb,
agent_blast_strength=info["agent_blast_strength"])
# agents might be disappeared, because of overestimated bombs
for t, positions in enumerate(list_enemy_positions):
list_boards[my_action][t][positions] = constants.Item.AgentDummy.value
# some bombs may explode with extra bombs, leading to under estimation
for t in range(len(list_boards[my_action])):
flame_positions = np.where(info["list_boards_no_move"][t] == constants.Item.Flames.value)
list_boards[my_action][t][flame_positions] = constants.Item.Flames.value
"""
processed = Parallel(n_jobs=-1, verbose=0)(
[delayed(search_time_expanded_network)(list_boards[action][1:], my_next_position[action], action)
for action in list_boards]
)
for survivable, my_action in processed:
if my_next_position[my_action] in survivable[0]:
is_survivable[my_action] = True
n_survivable[my_action] = [1] + [len(s) for s in survivable[1:]]
"""
for my_action in list_boards:
survivable = search_time_expanded_network(list_boards[my_action][1:],
my_next_position[my_action])
if my_next_position[my_action] in survivable[0]:
is_survivable[my_action] = True
n_survivable[my_action] = [1] + [len(s) for s in survivable[1:]]
survivable_actions = list()
for a in is_survivable:
if not is_survivable[a]:
continue
if might_blocked[a] and not is_survivable[constants.Action.Stop]:
continue
if n_survivable[a][-1] <= 1:
is_survivable[a] = False
continue
survivable_actions.append(a)
#
# Choose action
#
if len(survivable_actions) == 0:
#
# return None, if no survivable actions
#
return None
elif len(survivable_actions) == 1:
#
# Choose the survivable action, if it is the only choice
#
action = survivable_actions[0]
if verbose:
print("The only survivable action", action)
return action.value
#
# Bomb at a target
#
# fraction of blocked node in the survival trees of enemies
_list_boards = deepcopy(info["list_boards_no_move"])
if obs["bomb_blast_strength"][my_position]:
for b in _list_boards:
if utility.position_is_agent(b, my_position):
b[my_position] = constants.Item.Bomb.value
else:
for b in _list_boards:
if utility.position_is_agent(b, my_position):
b[my_position] = constants.Item.Passage.value
total_frac_blocked, n_survivable_nodes, blocked_time_positions \
= self._get_frac_blocked(_list_boards, my_enemies, board, obs["bomb_life"])
if teammate_position is not None:
total_frac_blocked_teammate, n_survivable_nodes_teammate, blocked_time_positions_teammate \
= self._get_frac_blocked(_list_boards, [my_teammate], board, obs["bomb_life"])
"""
np.set_printoptions(precision=3)
print("enemy")
print(total_frac_blocked)
print("teammate")
print(total_frac_blocked_teammate)
print("product")
prod = total_frac_blocked * (1 - total_frac_blocked_teammate)
print(prod[:5,:5])
"""
p_survivable = defaultdict(float)
for action in n_survivable:
p_survivable[action] = sum(n_survivable[action]) / self._my_survivability_threshold
if p_survivable[action] > 1:
p_survivable[action] = 1
block = defaultdict(float)
for action in [constants.Action.Stop,
constants.Action.Up, constants.Action.Down,
constants.Action.Left, constants.Action.Right]:
next_position = my_next_position[action]
if next_position is None:
continue
if next_position in set.union(kickable, might_kickable):
# kick will be considered later
continue
if all([utility.position_is_flames(board, next_position),
info["flame_life"][next_position] > 1,
is_survivable[constants.Action.Stop]]):
# if the next position is flames,
# I want to stop to wait, which must be feasible
block[action] = total_frac_blocked[next_position] * p_survivable[constants.Action.Stop]
if teammate_position is not None:
block[action] *= (1 - total_frac_blocked_teammate[next_position])
block[action] *= self._inv_tmp
block[action] -= np.log(-np.log(self.random.uniform()))
continue
elif not is_survivable[action]:
continue
if all([might_blocked[action],
not is_survivable[constants.Action.Stop]]):
continue
block[action] = total_frac_blocked[next_position] * p_survivable[action]
if teammate_position is not None:
block[action] *= (1 - total_frac_blocked_teammate[next_position])
block[action] *= self._inv_tmp
block[action] -= np.log(-np.log(self.random.uniform()))
if might_blocked[action]:
block[action] = (total_frac_blocked[my_position] * p_survivable[constants.Action.Stop]
+ total_frac_blocked[next_position] * p_survivable[action]) / 2
if teammate_position is not None:
block[action] *= (1 - total_frac_blocked_teammate[next_position])
block[action] *= self._inv_tmp
block[action] -= np.log(-np.log(self.random.uniform()))
if is_survivable[constants.Action.Bomb]:
list_boards_with_bomb, _ \
= self._board_sequence(board,
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
my_blast_strength=my_blast_strength,
my_action=constants.Action.Bomb)
n_survivable_nodes_with_bomb = defaultdict(int)
for enemy in my_enemies:
# get survivable tree of the enemy
rows, cols = np.where(board==enemy.value)
if len(rows) == 0:
continue
enemy_position = (rows[0], cols[0])
_survivable = search_time_expanded_network(list_boards_with_bomb,
enemy_position)
n_survivable_nodes_with_bomb[enemy] = sum([len(positions) for positions in _survivable])
n_with_bomb = sum([n_survivable_nodes_with_bomb[enemy] for enemy in my_enemies])
n_with_none = sum([n_survivable_nodes[enemy] for enemy in my_enemies])
if n_with_none == 0:
total_frac_blocked_with_bomb = 0
# place more bombs, so the stacked enemy cannot kick
x, y = my_position
for dx, dy in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
next_position = (x + dx, y + dy)
following_position = (x + 2 * dx, y + 2 * dy)
if not self._on_board(following_position):
continue
if all([obs["bomb_life"][next_position] > 0,
board[following_position] > constants.Item.AgentDummy.value]):
total_frac_blocked_with_bomb = 1
else:
total_frac_blocked_with_bomb = 1 - n_with_bomb / n_with_none
if teammate_position is not None:
# get survivable tree of the teammate
_survivable = search_time_expanded_network(list_boards_with_bomb, teammate_position)
n_survivable_nodes_with_bomb_teammate = sum([len(positions) for positions in _survivable])
n_with_bomb = n_survivable_nodes_with_bomb_teammate
n_with_none = n_survivable_nodes_teammate[my_teammate]
if n_with_none == 0:
total_frac_blocked_with_bomb_teammate = 0
else:
total_frac_blocked_with_bomb_teammate = 1 - n_with_bomb / n_with_none
action = constants.Action.Bomb
block[action] = total_frac_blocked_with_bomb * p_survivable[action]
if teammate_position is not None:
block[action] *= (1 - total_frac_blocked_with_bomb_teammate)
block[action] *= self._inv_tmp
block[action] -= np.log(-np.log(self.random.uniform()))
for next_position in kickable:
action = self._get_direction(my_position, next_position)
if not is_survivable[action]:
continue
list_boards_with_kick, _ \
= self._board_sequence(obs["board"],
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
my_action=action,
can_kick=True)
n_survivable_nodes_with_kick = defaultdict(int)
for enemy in my_enemies:
# get survivable tree of the enemy
rows, cols = np.where(board==enemy.value)
if len(rows) == 0:
continue
enemy_position = (rows[0], cols[0])
_survivable = search_time_expanded_network(list_boards_with_kick,
enemy_position)
n_survivable_nodes_with_kick[enemy] = sum([len(positions) for positions in _survivable])
n_with_kick = sum([n_survivable_nodes_with_kick[enemy] for enemy in my_enemies])
n_with_none = sum([n_survivable_nodes[enemy] for enemy in my_enemies])
if n_with_none == 0:
total_frac_blocked[next_position] = 0
else:
total_frac_blocked[next_position] = 1 - n_with_kick / n_with_none
if teammate_position is not None:
# get survivable tree of the teammate
_survivable = search_time_expanded_network(list_boards_with_kick, teammate_position)
n_survivable_nodes_with_kick_teammate = sum([len(positions) for positions in _survivable])
n_with_kick = n_survivable_nodes_with_kick_teammate
n_with_none = n_survivable_nodes_teammate[my_teammate]
if n_with_none == 0:
total_frac_blocked_teammate[next_position] = 0
else:
total_frac_blocked_teammate[next_position] = 1 - n_with_kick / n_with_none
block[action] = total_frac_blocked[next_position] * p_survivable[action]
if teammate_position is not None:
block[action] *= (1 - total_frac_blocked_teammate[next_position])
block[action] *= self._inv_tmp
block[action] -= np.log(-np.log(self.random.uniform()))
max_block = -np.inf
best_action = None
for action in block:
if block[action] > max_block:
max_block = block[action]
best_action = action
if block[constants.Action.Bomb] > self._bomb_threshold * self._inv_tmp:
if teammate_position is not None:
teammate_safety = total_frac_blocked_with_bomb_teammate * n_survivable_nodes_with_bomb_teammate
if any([teammate_safety > self._teammate_survivability_threshold,
total_frac_blocked_with_bomb_teammate < self._interfere_threshold,
total_frac_blocked_with_bomb_teammate < total_frac_blocked_teammate[my_position]]):
teammate_ok = True
else:
teammate_ok = False
else:
teammate_ok = True
if teammate_ok:
if best_action == constants.Action.Bomb:
if verbose:
print("Bomb is best", constants.Action.Bomb)
return constants.Action.Bomb.value
if best_action == constants.Action.Stop:
if verbose:
print("Place a bomb at a locally optimal position", constants.Action.Bomb)
return constants.Action.Bomb.value
#
# Move towards where to bomb
#
if best_action not in [None, constants.Action.Bomb]:
next_position = my_next_position[best_action]
should_chase = (total_frac_blocked[next_position] > self._chase_threshold)
if teammate_position is not None:
teammate_safety = total_frac_blocked_teammate[next_position] * n_survivable_nodes_teammate[my_teammate]
if any([teammate_safety > self._teammate_survivability_threshold,
total_frac_blocked_teammate[next_position] < self._interfere_threshold,
total_frac_blocked_teammate[next_position] < total_frac_blocked_teammate[my_position]]):
teammate_ok = True
else:
teammate_ok = False
else:
teammate_ok = True
if should_chase and teammate_ok:
if all([utility.position_is_flames(board, next_position),
info["flame_life"][next_position] > 1,
is_survivable[constants.Action.Stop]]):
action = constants.Action.Stop
if verbose:
print("Wait flames life", action)
return action.value
else:
if verbose:
print("Move towards better place to bomb", best_action)
return best_action.value
# Exclude the action representing stop to wait
max_block = -np.inf
best_action = None
for action in survivable_actions:
if block[action] > max_block:
max_block = block[action]
best_action = action
#
# Do not take risky actions
#
most_survivable_action = self._action_most_survivable(n_survivable)
# ignore actions with low survivability
_survivable_actions = list()
for action in n_survivable:
n = sum(n_survivable[action])
if not is_survivable[action]:
continue
elif n > self._my_survivability_threshold:
_survivable_actions.append(action)
else:
print("RISKY", action)
is_survivable[action] = False
if len(_survivable_actions) > 1:
survivable_actions = _survivable_actions
elif best_action is not None:
if verbose:
print("Take the best action in danger", best_action)
return best_action.value
else:
# Take the most survivable action
if verbose:
print("Take the most survivable action", most_survivable_action)
return most_survivable_action.value
#
# Do not interfere with teammate
#
if all([teammate_position is not None,
len(enemy_positions) > 0 or len(info["curr_bombs"]) > 0]):
# ignore actions that interfere with teammate
min_interfere = np.inf
least_interfere_action = None
_survivable_actions = list()
for action in survivable_actions:
if action == constants.Action.Bomb:
frac = total_frac_blocked_with_bomb_teammate
else:
next_position = my_next_position[action]
frac = total_frac_blocked_teammate[next_position]
if frac < min_interfere:
min_interfere = frac
least_interfere_action = action
if frac < self._interfere_threshold:
_survivable_actions.append(action)
else:
print("INTERFERE", action)
is_survivable[action] = False
if len(_survivable_actions) > 1:
survivable_actions = _survivable_actions
elif best_action is not None:
# Take the least interfering action
if verbose:
print("Take the best action in intereference", best_action)
return best_action.value
else:
if verbose:
print("Take the least interfering action", least_interfere_action)
return least_interfere_action.value
consider_bomb = True
if not is_survivable[constants.Action.Bomb]:
consider_bomb = False
#
# Find reachable items
#
# List of boards simulated
list_boards, _ = self._board_sequence(board,
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
enemy_mobility=self._enemy_mobility,
enemy_bomb=self._enemy_bomb,
agent_blast_strength=info["agent_blast_strength"])
# some bombs may explode with extra bombs, leading to under estimation
for t in range(len(list_boards)):
flame_positions = np.where(info["list_boards_no_move"][t] == constants.Item.Flames.value)
list_boards[t][flame_positions] = constants.Item.Flames.value
# List of the set of survivable time-positions at each time
# and preceding positions
survivable, prev, _, _ = self._search_time_expanded_network(list_boards,
my_position)
if len(survivable[-1]) == 0:
survivable = [set() for _ in range(len(survivable))]
# Items and bomb target that can be reached in a survivable manner
_, _, next_to_items \
= self._find_reachable_items(list_boards,
my_position,
survivable)
#
# If I have seen an enemy recently and cannot see him now, them move to the last seen position
#
action = self._action_to_enemy(my_position, next_to_items[constants.Item.Fog], prev, is_survivable, info,
my_enemies)
if action is not None:
if verbose:
print("Moving toward last seen enemy", action)
return action.value
#
# If I have seen a teammate recently, them move away from the last seen position
#
action = self._action_away_from_teammate(my_position,
next_to_items[constants.Item.Fog],
prev,
is_survivable,
info,
my_teammate)
if action is not None:
if verbose:
print("Moving away from last seen teammate", action)
return action.value
#
# Move towards a fog where we have not seen longest
#
action = self._action_to_fog(my_position, next_to_items[constants.Item.Fog], prev, is_survivable, info)
if action is not None:
#if True:
if self.random.uniform() < 0.8:
if verbose:
print("Moving toward oldest fog", action)
return action.value
#
# Choose most survivable action
#
max_block = -np.inf
best_action = None
for action in survivable_actions:
if action == constants.Action.Bomb:
continue
if block[action] > max_block:
max_block = block[action]
best_action = action
if verbose:
print("Take the best action among safe actions (nothing else to do)", best_action)
if best_action is None:
# this should not be the case
return None
else:
return best_action.value
def act(self, obs, action_space):
# The number of steps
self._steps += 1
# Collapse the board if just collapsed in the previous step
info = dict()
info["steps"] = self._steps
info["recently_seen"] = deepcopy(obs["board"])
if self._just_collapsed is not None:
L = self._just_collapsed
U = obs["board"].shape[0] - 1 - L
flames_positions = np.where(
obs["board"] == constants.Item.Flames.value)
info["recently_seen"][L, :][L:U + 1] = constants.Item.Rigid.value
info["recently_seen"][U, :][L:U + 1] = constants.Item.Rigid.value
info["recently_seen"][:, L][L:U + 1] = constants.Item.Rigid.value
info["recently_seen"][:, U][L:U + 1] = constants.Item.Rigid.value
info["recently_seen"][
flames_positions] = constants.Item.Flames.value
obs["bomb_life"][L, :][L:U + 1] = 0
obs["bomb_life"][U, :][L:U + 1] = 0
obs["bomb_life"][:, L][L:U + 1] = 0
obs["bomb_life"][:, U][L:U + 1] = 0
obs["bomb_blast_strength"][L, :][L:U + 1] = 0
obs["bomb_blast_strength"][U, :][L:U + 1] = 0
obs["bomb_blast_strength"][:, L][L:U + 1] = 0
obs["bomb_blast_strength"][:, U][L:U + 1] = 0
#
# Whether each location is Rigid
#
# False may be an unknown
#
if self._num_rigid_found < constants.NUM_RIGID:
self._is_rigid += (obs["board"] == constants.Item.Rigid.value)
self._is_rigid += (obs["board"].T == constants.Item.Rigid.value)
self._num_rigid_found = np.sum(self._is_rigid)
self._unreachable = ~self._get_reachable(self._is_rigid)
self._unreachable_locations = np.where(self._unreachable)
#
# What we have seen last time, and how many steps have past since then
#
visible_locations = np.where(obs["board"] != constants.Item.Fog.value)
self._last_seen[visible_locations] = obs["board"][visible_locations]
self._last_seen[
self._unreachable_locations] = constants.Item.Rigid.value
self._since_last_seen += 1
self._since_last_seen[visible_locations] = 0
self._since_last_seen[np.where(self._is_rigid)] = 0
if self._steps == 0:
# We have some knowledge about the initial configuration of the board
C = constants.BOARD_SIZE - 2
self._last_seen[(1, 1)] = constants.Item.Agent0.value
self._last_seen[(C, 1)] = constants.Item.Agent1.value
self._last_seen[(C, C)] = constants.Item.Agent2.value
self._last_seen[(1, C)] = constants.Item.Agent3.value
rows = np.array([1, C, 1, C])
cols = np.array([1, 1, C, C])
self._since_last_seen[(rows, cols)] = 0
rows = np.array([
1, 1, 1, 1, 2, 3, C - 1, C - 2, C, C, C, C, 2, 3, C - 1, C - 2
])
cols = np.array([
2, 3, C - 1, C - 2, 1, 1, 1, 1, 2, 3, C - 1, C - 2, C, C, C, C
])
self._last_seen[(rows, cols)] = constants.Item.Passage.value
self._since_last_seen[(rows, cols)] = 0
#
# We know exactly how my teamate is digging
#
my_position = obs["position"]
if self._steps == 33:
passage_under_fog \
= (self._last_seen.T == constants.Item.Passage.value) * (self._last_seen == constants.Item.Fog.value)
positions = np.where(passage_under_fog)
self._last_seen[positions] = constants.Item.Passage.value
self._since_last_seen[positions] = 0
info["since_last_seen"] = self._since_last_seen
info["last_seen"] = self._last_seen
if not self._just_collapsed:
# then we do not see the true board, so skip
recently_seen_positions = (info["since_last_seen"] <
self._use_last_seen)
info["recently_seen"][recently_seen_positions] = info["last_seen"][
recently_seen_positions]
# TODO: deepcopy are not needed with Docker
board = info["recently_seen"]
bomb_life = obs["bomb_life"]
bomb_blast_strength = obs["bomb_blast_strength"]
my_enemies = [
constants.Item(e) for e in obs['enemies']
if e != constants.Item.AgentDummy
]
if obs["teammate"] != constants.Item.AgentDummy:
my_teammate = obs["teammate"]
else:
my_teammate = None
info["prev_action"] = self._prev_action
info["prev_position"] = self._prev_position
#
# Modify the board
#
board[self._unreachable_locations] = constants.Item.Rigid.value
#
# Summarize information about bombs
#
# curr_bombs : list of current bombs
# moving_direction : array of moving direction of bombs
info["curr_bombs"], info["moving_direction"] \
= self._get_bombs(obs["board"], # use observation to keep the bombs under fog
bomb_blast_strength, self._prev_bomb_blast_strength,
bomb_life, self._prev_bomb_life)
self._prev_bomb_life = bomb_life.copy()
self._prev_bomb_blast_strength = bomb_blast_strength.copy()
#
# Bombs to be exploded in the next step
#
curr_bomb_position_strength = list()
rows, cols = np.where(bomb_blast_strength > 0)
for position in zip(rows, cols):
strength = int(bomb_blast_strength[position])
curr_bomb_position_strength.append((position, strength))
#
# Summarize information about flames
#
if self._prev_board is not None:
info["curr_flames"], self._prev_flame_life \
= self._get_flames(obs["board"], # use observation to keep the bombs under fog
self._prev_board[-1],
self._prev_flame_life,
self._prev_bomb_position_strength,
curr_bomb_position_strength,
self._prev_moving_direction)
else:
info["curr_flames"] = []
info["flame_life"] = self._prev_flame_life
self._prev_moving_direction = deepcopy(info["moving_direction"])
self._prev_bomb_position_strength = curr_bomb_position_strength
#
# List of simulated boards, assuming enemies stay unmoved
#
step_to_collapse = None
collapse_ring = None
if obs["game_env"] == 'pommerman.envs.v1:Pomme':
# Collapse mode
# cannot trust the board just collapsed, so skip
if self._just_collapsed is None:
already_collapsed = (self._when_collapse < self._steps)
not_rigid = (obs["board"] != constants.Item.Rigid.value) * (
obs["board"] != constants.Item.Fog.value)
not_collapsed_positions = np.where(already_collapsed *
not_rigid)
self._when_collapse[not_collapsed_positions] = np.inf
collapse_steps = [
step for step in self._collapse_steps if step >= self._steps
]
if len(collapse_steps):
step_to_collapse = min(collapse_steps) - self._steps
collapse_ring = len(self._collapse_steps) - len(collapse_steps)
if step_to_collapse == 0:
self._just_collapsed = collapse_ring
else:
self._just_collapsed = None
else:
self._just_collapsed = None
info["step_to_collapse"] = step_to_collapse
info["collapse_ring"] = collapse_ring
info["list_boards_no_move"], _ \
= self._board_sequence(board,
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
enemy_mobility=0,
step_to_collapse=step_to_collapse,
collapse_ring=collapse_ring)
#
# Might appear item from flames
#
info["might_powerup"] = np.full(self.board_shape, False)
if self._prev_board is None:
# Flame life is 2
# flame life is hardcoded in pommmerman/characters.py class Flame
self._prev_board = [
deepcopy(board),
deepcopy(board),
deepcopy(board)
]
else:
old_board = self._prev_board.pop(0)
self._prev_board.append(deepcopy(board))
if self._might_remaining_powerup:
# was wood and now flames
was_wood = (old_board == constants.Item.Wood.value)
now_flames = (board == constants.Item.Flames.value)
info["might_powerup"] = was_wood * now_flames
# now wood and will passage
now_wood = (board == constants.Item.Wood.value)
become_passage = (info["list_boards_no_move"][-1] ==
constants.Item.Passage.value)
info["might_powerup"] += now_wood * become_passage
maybe_powerup = info["might_powerup"] \
+ (self._last_seen == constants.Item.Wood.value) \
+ (self._last_seen == constants.Item.ExtraBomb.value) \
+ (self._last_seen == constants.Item.IncrRange.value) \
+ (self._last_seen == constants.Item.Kick.value)
if not maybe_powerup.any():
self._might_remaining_powerup = False
# update the estimate of enemy blast strength
rows, cols = np.where(bomb_life == constants.DEFAULT_BOMB_LIFE - 1)
for position in zip(rows, cols):
if position == my_position:
continue
enemy = board[position]
self._agent_blast_strength[enemy] = bomb_blast_strength[position]
info["agent_blast_strength"] = self._agent_blast_strength
# enemy positions
info["enemy_positions"] = list()
for enemy in my_enemies:
rows, cols = np.where(board == enemy.value)
if len(rows) == 1:
info["enemy_positions"].append((rows[0], cols[0]))
elif len(rows) > 1:
# choose the most recently seen enemy of this ID, because only one
time_passed = info["since_last_seen"][(rows, cols)]
idx = np.argmin(time_passed)
enemy_position = (rows[idx], cols[idx])
board[(
rows, cols
)] = constants.Item.Passage.value # overwrite old teammates by passage
board[enemy_position] = enemy.value
info["enemy_positions"].append(enemy_position)
# teammate position
info["teammate_position"] = None
if my_teammate is not None:
rows, cols = np.where(board == my_teammate.value)
if len(rows) == 1:
info["teammate_position"] = (rows[0], cols[0])
elif len(rows) > 1:
# choose the most recently seen teammate, because only one
time_passed = info["since_last_seen"][(rows, cols)]
idx = np.argmin(time_passed)
info["teammate_position"] = (rows[idx], cols[idx])
board[(
rows, cols
)] = constants.Item.Passage.value # overwrite old teammates by passage
board[info["teammate_position"]] = my_teammate.value
# next positions
info["my_next_position"] = {constants.Action.Stop: my_position}
if all([obs["ammo"] > 0,
obs["bomb_blast_strength"][my_position] == 0]):
info["my_next_position"][constants.Action.Bomb] = my_position
else:
info["my_next_position"][constants.Action.Bomb] = None
for action in [
constants.Action.Up, constants.Action.Down,
constants.Action.Left, constants.Action.Right
]:
next_position = self._get_next_position(my_position, action)
if self._on_board(next_position):
if board[next_position] in [
constants.Item.Rigid.value, constants.Item.Wood.value
]:
info["my_next_position"][action] = None
else:
info["my_next_position"][action] = next_position
else:
info["my_next_position"][action] = None
# kickable positions
if obs["can_kick"]:
is_bomb = np.full(self.board_shape, False)
is_bomb[np.where(obs["bomb_blast_strength"] > 0)] = True
info["kickable"], info["might_kickable"] \
= self._kickable_positions(obs, is_bomb, info["moving_direction"])
info["all_kickable"] = set.union(info["kickable"],
info["might_kickable"])
else:
info["kickable"] = set()
info["might_kickable"] = set()
info["all_kickable"] = set()
# might block/blocked actions
# I am the leader if agent0 or agent1
# I am the follower otherwise
# If leader, not blocked by teammate
# If follower, do not block teammate
info["might_blocked"] = self._get_might_blocked(
board, my_position, info["enemy_positions"],
info["might_kickable"])
if all([
board[my_position]
in [constants.Item.Agent2.value, constants.Item.Agent3.value],
info["teammate_position"] is not None
]):
info["might_block_teammate"] = self._get_might_blocked(
board, my_position, [info["teammate_position"]],
info["might_kickable"])
else:
info["might_block_teammate"] = defaultdict(bool)
info["might_block_actions"] = set([
a for a in info["might_block_teammate"]
if info["might_block_teammate"][a]
])
#
# Choose a slave to act
#
if self._isolated:
is_wood_visible = (constants.Item.Wood.value in board)
is_closed = self._is_closed(board, my_position)
if any([not is_wood_visible, not is_closed]):
self._isolated = False
action = None
if self._isolated:
# Act with an agent who do not consider other dypmAgents
action = self.isolated_slave.act(obs, action_space, info)
elif not self._might_remaining_powerup:
# Act with an agent who do not consider powerups
if obs["game_env"] == 'pommerman.envs.v1:Pomme':
info["escape"] = (self._when_collapse == np.inf) * (
info["last_seen"] == constants.Item.Passage.value)
action = self.battle_slave.act(obs, action_space, info)
else:
action = self.generic_slave.act(obs, action_space, info)
if action is None:
# Act with a special agent, who only seeks to survive
action = self.surviving_slave.act(obs, action_space, info)
self._prev_action = action
self._prev_position = my_position
return action
def obs_to_net_in(self, obs):
"""
Takes the observation dictionary and turns value into feature planes of same shape
"""
# Handle order of the keys
key_list = [
"bomb_life",
"bomb_blast_strength",
"bomb_moving_direction",
"flame_life",
"blast_strength",
"can_kick",
"ammo",
#"game_type",
#"game_env",
#"step_count",
#"alive",
#"position",
#"teammate",
#"enemies",
#"message",
"board"
]
type_dict = {
# Full board to break down
"board": "full_board",
# Board type values:
"bomb_blast_strength": "board",
"bomb_life": "board",
"bomb_moving_direction": "board",
"flame_life": "board",
# Single value type values:
"blast_strength": "val",
"can_kick": "val",
"ammo": "val",
"game_type": "val",
"game_env": "val",
"step_count": "val",
# Special type values:
"alive": None,
"position": "pos",
"message": "pos",
"teammate": "mate",
"enemies": "enemy"
}
# Rating of items (Prefere power-ups, avoid flames)
desire_dict = {
constants.Item.ExtraBomb.value: 0,
constants.Item.Kick.value: 0,
constants.Item.IncrRange.value: 0,
constants.Item.Wood.value: 1,
constants.Item.Passage.value: 2,
constants.Item.Fog.value: 3,
constants.Item.Rigid.value: 5,
constants.Item.Agent0.value: 6,
constants.Item.Agent1.value: 6,
constants.Item.Agent2.value: 6,
constants.Item.Agent3.value: 6,
constants.Item.AgentDummy.value: 6,
constants.Item.Bomb.value: 7,
constants.Item.Flames.value: 8
}
board_shape = obs['board'].shape
agents = [
constants.Item.Agent0, constants.Item.Agent1,
constants.Item.Agent2, constants.Item.Agent3,
constants.Item.AgentDummy
]
enemies = obs['enemies']
mate = obs['teammate']
alive = obs['alive']
player = [constants.Item(e) for e in alive]
for e in enemies:
desire_dict[e.value] = 4
try:
player.remove(e)
except:
pass
try:
player.remove(mate)
except:
pass
net_in = np.zeros(
(*board_shape, len(key_list) + len(constants.Item) - 1))
for idx, key in enumerate(key_list):
# Determine current value_type to handle value accordingly
value = obs[key]
value_type = type_dict[key]
if value_type == 'full_board':
# Break board down in its different features
desire_plane = np.zeros(shape=board_shape, dtype=np.float32)
enemy_plane = np.zeros(shape=board_shape, dtype=np.float32)
mate_plane = np.zeros(shape=board_shape, dtype=np.float32)
self_plane = np.zeros(shape=board_shape, dtype=np.float32)
i = 0
# One-hot encode every item
for e in constants.Item:
feature_plane = np.where(
value == e.value,
np.ones(shape=board_shape, dtype=np.float32),
np.zeros(shape=board_shape, dtype=np.float32))
if e in agents:
if e in enemies:
enemy_plane += feature_plane
if e == mate:
mate_plane += feature_plane
if e in player:
self_plane = feature_plane
else:
net_in[:, :, idx + i] = feature_plane
i += 1
# Encode desired positions
desire_plane += feature_plane * desire_dict[e.value]
if self.last_board is None:
self.last_board = desire_plane.copy()
net_in[:, :, idx + i] = enemy_plane
i += 1
net_in[:, :, idx + i] = mate_plane
i += 1
net_in[:, :, idx + i] = self_plane
i += 1
net_in[:, :, idx + i] = self.last_board
net_in[:, :, -1] = desire_plane
self.last_board = desire_plane.copy()
else:
if value_type == 'board':
# Value already board_shape
feature_plane = np.array(value, dtype=np.float32)
elif value_type == 'val':
# Fill array of board_shape with value
feature_plane = np.zeros(shape=board_shape,
dtype=np.float32)
feature_plane.fill(np.float32(value))
elif value_type == 'pos':
# Mark position in zero plane
feature_plane = np.zeros(shape=board_shape,
dtype=np.float32)
feature_plane[value[0], value[1]] = 1
elif value_type == 'mate':
# Fill array of board_shape with mate value for alive mate
feature_plane = np.zeros(shape=board_shape,
dtype=np.float32)
if value.value in obs['alive']:
feature_plane.fill(np.float32(value.value))
elif value_type == 'enemy':
# Sum up enemy values for alive enemies
feature_plane = np.zeros(shape=board_shape,
dtype=np.float32)
for val in value:
if val.value in obs['alive']:
feature_plane += val.value
else:
# Create zero plane if value is not handled
feature_plane = np.zeros(shape=board_shape,
dtype=np.float32)
net_in[:, :, idx] = feature_plane
return net_in
def act(self, obs, action_space, info):
#
# Definitions
#
board = info['last_seen']
#board = obs['board']
my_position = obs["position"] # tuple([x,y]): my position
my_ammo = obs['ammo'] # int: the number of bombs I have
my_blast_strength = obs['blast_strength']
my_kick = obs["can_kick"] # whether I can kick
my_enemies = [constants.Item(e) for e in obs['enemies']]
my_teammate = obs["teammate"]
kickable, might_kickable \
= self._kickable_positions(obs, info["moving_direction"],
consider_agents=True)
# enemy positions
enemy_positions = list()
for enemy in my_enemies:
rows, cols = np.where(board == enemy.value)
if len(rows) == 0:
continue
enemy_positions.append((rows[0], cols[0]))
# teammate position
teammate_position = None
if my_teammate is not None:
rows, cols = np.where(board == my_teammate.value)
if len(rows):
teammate_position = (rows[0], cols[0])
# positions that might be blocked
if teammate_position is None:
agent_positions = enemy_positions
else:
agent_positions = enemy_positions + [teammate_position]
might_blocked = self._get_might_blocked(board, my_position,
agent_positions,
might_kickable)
#
# Survivability, when enemy is replaced by a bomb, and no move afterwards
#
# replace enemy with bomb
_bombs = deepcopy(info["curr_bombs"])
rows, cols = np.where(board > constants.Item.AgentDummy.value)
for position in zip(rows, cols):
if board[position] not in my_enemies:
continue
if obs["bomb_blast_strength"][position]:
# already a bomb
continue
bomb = characters.Bomb(
characters.Bomber(), # dummy owner of the bomb
position,
constants.DEFAULT_BOMB_LIFE,
enemy_blast_strength_map[position],
None)
_bombs.append(bomb)
n_survivable_bomb = self._get_n_survivable(board,
_bombs,
info["curr_flames"],
obs,
my_position,
set.union(
kickable,
might_kickable),
enemy_mobility=0)
#
# Survivability, when enemy moves one position or stay unmoved
#
n_survivable_move = self._get_n_survivable(board,
info["curr_bombs"],
info["curr_flames"],
obs,
my_position,
set.union(
kickable,
might_kickable),
enemy_mobility=1)
#
# Survivability, when no enemies
#
_board = deepcopy(board)
agent_positions = np.where(_board > constants.Item.AgentDummy.value)
_board[agent_positions] = constants.Item.Passage.value
_board[my_position] = board[my_position]
_obs = {
"position": obs["position"],
"blast_strength": obs["blast_strength"],
"ammo": obs["ammo"],
"bomb_life": obs["bomb_life"],
"board": _board
}
n_survivable_none = self._get_n_survivable(_board,
info["curr_bombs"],
info["curr_flames"],
_obs,
my_position,
set.union(
kickable,
might_kickable),
enemy_mobility=0)
#
# Survivable actions
#
survivable_actions_bomb = set(
[a for a in n_survivable_bomb if n_survivable_bomb[a][-1] > 0])
survivable_actions_move = set(
[a for a in n_survivable_move if n_survivable_move[a][-1] > 0])
survivable_actions_none = set(
[a for a in n_survivable_none if n_survivable_none[a][-1] > 0])
survivable_actions = set.intersection(survivable_actions_bomb,
survivable_actions_move,
survivable_actions_none)
# if can survive without possibility of being blocked, then do so
if not constants.Action.Stop in survivable_actions:
_survivable_actions = [
action for action in survivable_actions
if not might_blocked[action]
]
if len(_survivable_actions):
survivable_action = _survivable_actions
_survivable_actions_bomb = [
action for action in survivable_actions_bomb
if not might_blocked[action]
]
_survivable_actions_move = [
action for action in survivable_actions_move
if not might_blocked[action]
]
_survivable_actions_none = [
action for action in survivable_actions_none
if not might_blocked[action]
]
if all([
len(_survivable_actions_bomb) > 0,
len(_survivable_actions_move) > 0,
len(_survivable_actions_none) > 0
]):
survivable_action_bomb = _survivable_actions_bomb
survivable_action_move = _survivable_actions_move
survivable_action_none = _survivable_actions_none
#
# Choose actions
#
if len(survivable_actions) == 1:
action = survivable_actions.pop()
if verbose:
print("Only survivable action", action)
return action.value
if len(survivable_actions) > 1:
n_survivable_expected = dict()
for a in survivable_actions:
if might_blocked[a]:
n_survivable_expected[a] \
= np.array(n_survivable_bomb[a]) \
+ np.array(n_survivable_move[constants.Action.Stop]) \
+ np.array(n_survivable_none[a])
n_survivable_expected[a] = n_survivable_expected[a] / 3
elif a in [constants.Action.Stop, constants.Action.Bomb]:
n_survivable_expected[a] = np.array(
n_survivable_bomb[a]) + np.array(n_survivable_move[a])
n_survivable_expected[a] = n_survivable_expected[a] / 2
else:
n_survivable_expected[a] = np.array(
n_survivable_bomb[a]) + np.array(n_survivable_none[a])
n_survivable_expected[a] = n_survivable_expected[a] / 2
action = self._get_most_survivable_action(n_survivable_expected)
if verbose:
print("Most survivable action", action)
return action.value
# no survivable actions for all cases
survivable_actions = set(
list(n_survivable_bomb.keys()) + list(n_survivable_move.keys()) +
list(n_survivable_none.keys()))
if len(survivable_actions) == 1:
action = survivable_actions.pop()
if verbose:
print("Only might survivable action", action)
return action.value
if len(survivable_actions) > 1:
for a in set.union(survivable_actions, {constants.Action.Stop}):
if a not in n_survivable_bomb:
n_survivable_bomb[a] = np.zeros(self._search_range)
if a not in n_survivable_move:
n_survivable_move[a] = np.zeros(self._search_range)
if a not in n_survivable_none:
n_survivable_none[a] = np.zeros(self._search_range)
n_survivable_expected = dict()
for a in survivable_actions:
if might_blocked[a]:
n_survivable_expected[a] \
= np.array(n_survivable_bomb[a]) \
+ np.array(n_survivable_move[constants.Action.Stop]) \
+ np.array(n_survivable_none[a])
n_survivable_expected[a] = n_survivable_expected[a] / 3
elif a in [constants.Action.Stop, constants.Action.Bomb]:
n_survivable_expected[a] = np.array(
n_survivable_bomb[a]) + np.array(n_survivable_move[a])
n_survivable_expected[a] = n_survivable_expected[a] / 2
else:
n_survivable_expected[a] = np.array(
n_survivable_bomb[a]) + np.array(n_survivable_none[a])
n_survivable_expected[a] = n_survivable_expected[a] / 2
action = self._get_most_survivable_action(n_survivable_expected)
if verbose:
print("Most might survivable action", action)
return action.value
# no survivable action found for any cases
# TODO : Then consider killing enemies or helping teammate
# fraction of blocked node in the survival trees of enemies
_list_boards = deepcopy(info["list_boards_no_move"])
if obs["bomb_blast_strength"][my_position]:
for b in _list_boards:
if utility.position_is_agent(b, my_position):
b[my_position] = constants.Item.Bomb.value
else:
for b in _list_boards:
if utility.position_is_agent(b, my_position):
b[my_position] = constants.Item.Passage.value
total_frac_blocked, n_survivable_nodes, blocked_time_positions \
= self._get_frac_blocked(_list_boards, my_enemies, board, obs["bomb_life"])
if teammate_position is not None:
total_frac_blocked_teammate, n_survivable_nodes_teammate, blocked_time_positions_teammate \
= self._get_frac_blocked(_list_boards, [my_teammate], board, obs["bomb_life"])
block = defaultdict(float)
for action in [
constants.Action.Stop, constants.Action.Up,
constants.Action.Down, constants.Action.Left,
constants.Action.Right
]:
next_position = self._get_next_position(my_position, action)
if not self._on_board(next_position):
continue
if board[next_position] in [
constants.Item.Rigid.value, constants.Item.Wood.value
]:
continue
if next_position in set.union(kickable, might_kickable):
# kick will be considered later
continue
block[action] = total_frac_blocked[next_position]
if teammate_position is not None:
block[action] *= (1 -
total_frac_blocked_teammate[next_position])
block[action] *= self._inv_tmp
block[action] -= np.log(-np.log(self.random.uniform()))
if any([my_ammo > 0, obs["bomb_blast_strength"][my_position] == 0]):
list_boards_with_bomb, _ \
= self._board_sequence(board,
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
my_blast_strength=my_blast_strength,
my_action=constants.Action.Bomb)
block[constants.Action.Bomb] \
= self._get_frac_blocked_two_lists(list_boards_with_bomb,
n_survivable_nodes,
board,
my_enemies)
if teammate_position is not None:
block_teammate = self._get_frac_blocked_two_lists(
list_boards_with_bomb, n_survivable_nodes, board,
[my_teammate])
block[constants.Action.Bomb] *= (1 - block_teammate)
block[constants.Action.Bomb] *= self._inv_tmp
block[constants.Action.Bomb] -= np.log(
-np.log(self.random.uniform()))
for next_position in set.union(kickable, might_kickable):
my_action = self._get_direction(my_position, next_position)
list_boards_with_kick, _ \
= self._board_sequence(obs["board"],
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
my_action=my_action,
can_kick=True)
block[my_action] \
= self._get_frac_blocked_two_lists(list_boards_with_kick,
n_survivable_nodes,
board,
my_enemies)
if teammate_position is not None:
block_teammate = self._get_frac_blocked_two_lists(
list_boards_with_kick, n_survivable_nodes, board,
[my_teammate])
block[my_action] *= (1 - block_teammate)
block[my_action] *= self._inv_tmp
block[my_action] -= np.log(-np.log(self.random.uniform()))
max_block = -np.inf
best_action = None
for action in block:
if block[action] > max_block:
max_block = block[action]
best_action = action
if best_action is not None:
if verbose:
print(
"Best action to kill enemies or help teammate (cannot survive)"
)
return best_action.value
# The following will not be used
if obs["ammo"] > 0 and obs["blast_strength"] == 0:
action = constants.Action.Bomb
if verbose:
print("Suicide", action)
return action.value
kickable_positions = list(set.union(kickable, might_kickable))
if kickable_positions:
self.random.shuffle(kickable_positions)
action = self._get_direction(my_position, kickable_positions[0])
if verbose:
print("Suicide kick", action)
return action.value
all_actions = [
constants.Action.Stop, constants.Action.Up, constants.Action.Down,
constants.Action.Right, constants.Action.Left
]
self.random.shuffle(all_actions)
for action in all_actions:
next_position = self._get_next_position(my_position, action)
if not self._on_board(next_position):
continue
if utility.position_is_wall(board, next_position):
continue
if verbose:
print("Random action", action)
return action.value
action = constants.Action.Stop
if verbose:
print("No action found", action)
return action.value
def act(self, obs, action_space, info):
#
# Definitions
#
board = obs['board']
my_position = obs["position"] # tuple([x,y]): my position
my_kick = obs["can_kick"] # whether I can kick
my_enemies = [constants.Item(e) for e in obs['enemies']]
my_teammate = obs["teammate"]
kickable, might_kickable \
= self._kickable_positions(obs, info["moving_direction"],
consider_agents=True)
#
# Survivability, when enemy is replaced by a bomb, and no move afterwards
#
# replace enemy with bomb
_bombs = deepcopy(info["curr_bombs"])
rows, cols = np.where(board > constants.Item.AgentDummy.value)
for position in zip(rows, cols):
if board[position] not in my_enemies:
continue
if obs["bomb_blast_strength"][position]:
# already a bomb
continue
bomb = characters.Bomb(
characters.Bomber(), # dummy owner of the bomb
position,
constants.DEFAULT_BOMB_LIFE,
enemy_blast_strength_map[position],
None)
_bombs.append(bomb)
n_survivable_bomb = self._get_n_survivable(board,
_bombs,
info["curr_flames"],
obs,
my_position,
set.union(
kickable,
might_kickable),
enemy_mobility=0)
print("survivable bomb")
for a in n_survivable_bomb:
print(a, n_survivable_bomb[a])
survivable_actions_bomb = set(n_survivable_bomb)
#
# Survivability, when enemy moves one position or stay unmoved
#
n_survivable_move = self._get_n_survivable(board,
info["curr_bombs"],
info["curr_flames"],
obs,
my_position,
set.union(
kickable,
might_kickable),
enemy_mobility=1)
# If my move is survivable with bomb but not with move,
# then my move must be blocked by an enemy.
# I might be blocked by an enemy with such my move,
# it will end up in stop and enemy is also stop,
# so my survivability with such my move should be the
# same as my survivability with stop when enemy stops
if constants.Action.Stop in survivable_actions_bomb:
for action in survivable_actions_bomb:
if action in [constants.Action.Stop, constants.Action.Bomb]:
continue
if action not in n_survivable_move:
n_survivable_move[action] = n_survivable_bomb[
constants.Action.Stop]
survivable_actions_move = set(n_survivable_move)
#print("survivable move")
#for a in n_survivable_move:
# print(a, n_survivable_move[a])
# if survivable by not stopping when enemy place a bomb,
# then do not stop
if survivable_actions_bomb - {constants.Action.Stop}:
survivable_actions_bomb -= {constants.Action.Stop}
survivable_actions_move -= {constants.Action.Stop}
#if survivable_actions_bomb - {constants.Action.Bomb}:
# survivable_actions_bomb -= {constants.Action.Bomb}
# survivable_actions_move -= {constants.Action.Bomb}
survivable_actions = set.intersection(survivable_actions_bomb,
survivable_actions_move)
#print("survivable", survivable_actions)
if len(survivable_actions) == 0:
if survivable_actions_bomb:
action = self._get_most_survivable_action(n_survivable_bomb)
print("Most survivable action when enemy place a bomb", action)
return action.value
elif survivable_actions_move:
action = self._get_most_survivable_action(n_survivable_move)
print("Most survivable action when enemy moves", action)
return action.value
else:
#
# Survivability with no enemies or teammate
#
_board = deepcopy(board)
agent_positions = np.where(
_board > constants.Item.AgentDummy.value)
_board[agent_positions] = constants.Item.Passage.value
_board[my_position] = board[my_position]
_obs = {
"position": obs["position"],
"blast_strength": obs["blast_strength"],
"ammo": obs["ammo"],
"bomb_life": obs["bomb_life"],
"board": _board
}
n_survivable = self._get_n_survivable(_board,
info["curr_bombs"],
info["curr_flames"],
_obs,
my_position,
set.union(
kickable,
might_kickable),
enemy_mobility=0)
survivable_actions = list(n_survivable)
if survivable_actions:
action = self._get_most_survivable_action(n_survivable)
print("Most survivable action when no enemy", action)
return action.value
else:
if obs["ammo"] > 0 and obs["blast_strength"] == 0:
action = constants.Action.Bomb
print("Suicide", action)
return action.value
else:
all_actions = [
constants.Action.Stop, constants.Action.Up,
constants.Action.Down, constants.Action.Right,
constants.Action.Left
]
random.shuffle(all_actions)
for action in all_actions:
next_position = self._get_next_position(
my_position, action)
if not self._on_board(next_position):
continue
if not utility.position_is_wall(
board, next_position):
continue
print("Random action", action)
return action.value
elif len(survivable_actions) == 1:
action = survivable_actions.pop()
print("Only survivable action", action)
return action.value
else:
n_survivable_min = dict()
for a in survivable_actions:
n_survivable_min[a] = min(
[n_survivable_bomb[a], n_survivable_move[a]])
action = self._get_most_survivable_action(n_survivable_min)
print("Most survivable action when no enemy", action)
return action.value
action = constants.Action.Stop
print("No action found", action)
return action.value
def _djikstra(board,
my_position,
bombs,
enemies,
bomb_timer=None,
depth=None,
exclude=None):
if depth is None:
depth = len(board) * 2
if exclude is None:
exclude = [
constants.Item.Fog, constants.Item.Rigid, constants.Item.Flames
]
def out_of_range(p1, p2):
x1, y1 = p1
x2, y2 = p2
return abs(y2 - y1) + abs(x2 - x1) > depth
items = defaultdict(list)
for bomb in bombs:
if bomb['position'] == my_position:
items[constants.Item.Bomb].append(my_position)
dist = {}
prev = {}
mx, my = my_position
for r in range(max(0, mx - depth), min(len(board), mx + depth)):
for c in range(max(0, my - depth), min(len(board), my + depth)):
position = (r, c)
if any([
out_of_range(my_position, position),
utility.position_in_items(board, position, exclude),
]):
continue
if position == my_position:
dist[position] = 0
else:
dist[position] = np.inf
prev[position] = None
item = constants.Item(board[position])
items[item].append(position)
# Djikstra
H = []
heapq.heappush(H, (0, my_position))
while H:
min_dist, position = heapq.heappop(H)
if (board[position] != constants.Item.Bomb.value
) and not utility.position_is_passable(board, position,
enemies):
continue
x, y = position
for row, col in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
new_position = (row + x, col + y)
if new_position not in dist:
continue
if not utility.position_is_passable(board, new_position,
enemies):
continue
if bomb_timer is not None:
t = bomb_timer[new_position]
if t > 0 and abs((min_dist + 1) - t) < 2:
continue
if min_dist + 1 < dist[new_position]:
dist[new_position] = min_dist + 1
prev[new_position] = position
heapq.heappush(H, (dist[new_position], new_position))
return items, dist, prev
def act(self, obs, action_space):
def convert_bombs(bomb_map):
ret = []
locations = np.where(bomb_map > 0)
for r, c in zip(locations[0], locations[1]):
ret.append({
'position': (r, c),
'blast_strength': int(bomb_map[(r, c)])
})
return ret
depth = 20
my_position = tuple(obs['position'])
board = np.array(obs['board'])
bombs = convert_bombs(np.array(obs['bomb_blast_strength']))
enemies = [constants.Item(e) for e in obs['enemies']]
ammo = int(obs['ammo'])
blast_strength = int(obs['blast_strength'])
if self.prev_pos != None:
if self.prev_pos == my_position:
if 1 <= self.prev_action.value <= 4:
if self.logging:
print('freeze')
board[self.prev_pos] = constants.Item.Rigid.value
items, dist, prev = self._djikstra(board,
my_position,
bombs,
enemies,
bomb_timer=self.bomb_time,
depth=depth)
if self.logging:
print('my_position =', my_position)
print('board =')
print(board)
print('dist =')
print(dist)
print('bombs =', bombs)
print('enemies =', enemies)
for e in enemies:
print(e)
pos = items.get(e, [])
print('pos =', pos)
print('pos_len=', len(pos))
if len(pos) > 0:
print('xy=', pos[0][0], ',', pos[0][1])
# print('pos_r =', x, ',',y)
print('ammo =', ammo)
print('blast_strength =', blast_strength)
test_ary = np.ones((11, 11))
for c in range(11):
for r in range(11):
if (r, c) in dist:
test_ary[r, c] = dist[(r, c)]
else:
test_ary[r, c] = -1
if self.logging:
print("dist_mat:")
print(test_ary)
# update bomb_time map
bomb_life = 8
has_bomb = {}
already_breakable = np.zeros((11, 11))
for b in bombs:
r, c = b['position']
strength = b['blast_strength']
# print('bomb_cr =', c, 'r=', r, 'st=', strength)
if self.bomb_time[(r, c)] == 0:
self.bomb_time[(r, c)] = bomb_life
else:
self.bomb_time[(r, c)] -= 1
for row, col in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
for d in range(1, strength):
new_pos = (r + d * row, c + d * col)
if TestSimpleAgent._out_of_board(new_pos):
continue
# if new_pos[0] < 0 or new_pos[0] > 10:
# continue
# if new_pos[1] < 0 or new_pos[1] > 10:
# continue
if utility.position_is_rigid(board, new_pos):
continue
if utility.position_is_wood(board, new_pos):
already_breakable[new_pos] = 1
if self.bomb_time[new_pos] == 0:
self.bomb_time[new_pos] = bomb_life
else:
self.bomb_time[new_pos] -= 1
has_bomb[new_pos] = 1
# clear up table
for c in range(11):
for r in range(11):
if (r, c) not in has_bomb:
self.bomb_time[(r, c)] = 0
if self.logging:
print("bomb_time:")
print(self.bomb_time)
# evaluate each position in terms of breakable woods
num_breakable = np.zeros((11, 11))
num_breakable_inside = np.zeros((11, 11))
for c in range(11):
for r in range(11):
if utility.position_is_wood(board, (r, c)):
if already_breakable[(r, c)]:
continue
for row, col in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
for d in range(1, blast_strength):
new_pos = (r + d * row, c + d * col)
if TestSimpleAgent._out_of_board(new_pos):
continue
if utility.position_is_passable(
board, new_pos,
enemies) or utility.position_is_flames(
board, new_pos):
num_breakable[new_pos] += 1
else:
break
tmp_num = 0
has_passable = False
for row, col in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
new_pos = (r + row, c + col)
if TestSimpleAgent._out_of_board(new_pos):
continue
if utility.position_is_wood(board, new_pos):
tmp_num += 1
elif utility.position_is_passable(
board, new_pos, enemies):
has_passable = True
if (not has_passable) and tmp_num > 0:
tmp_num -= 1
num_breakable_inside[(r, c)] = tmp_num
if self.logging:
print('num_breakable:')
print(num_breakable)
print('num_breakable_inside:')
print(num_breakable_inside)
num_breakable_total = np.zeros((11, 11))
for c in range(11):
for r in range(11):
num_breakable_total[(r, c)] = num_breakable[(r, c)]
if num_breakable_total[(r, c)] == -1 or num_breakable_total[(
r, c)] == np.inf:
continue
for row, col in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
new_pos = (r + row, c + col)
if new_pos[0] < 0 or new_pos[0] > 10:
continue
if new_pos[1] < 0 or new_pos[1] > 10:
continue
num_breakable_total[(
r, c)] += num_breakable_inside[new_pos] * 0.5
if self.logging:
print('num_breakable_total:')
print(num_breakable_total)
# evaluate each position in total
pos_scores = np.zeros((11, 11))
for c in range(11):
for r in range(11):
if (r, c) not in dist:
pos_scores[(r, c)] = -1
continue
elif dist[(r, c)] == np.inf:
pos_scores[(r, c)] = np.inf
continue
if num_breakable_total[(r, c)] > 0:
pos_scores[(r, c)] += num_breakable_total[(r, c)]
pos_scores[(r, c)] += (depth - dist[(r, c)]) * 0.2
# consider power-up items
if board[(r, c)] in {
constants.Item.ExtraBomb.value,
constants.Item.IncrRange.value
}:
pos_scores[(r, c)] += 50
if self.logging:
print('pos_score:')
print(pos_scores)
# consider degree of freedom
dis_to_ene = 100
for e in enemies:
pos = items.get(e, [])
if len(pos) > 0:
d = abs(pos[0][0] - my_position[0]) + abs(pos[0][1] -
my_position[1])
if dis_to_ene > d:
dis_to_ene = d
if dis_to_ene <= -4:
# if direction is not None:
deg_frees = np.zeros((11, 11))
for c in range(11):
for r in range(11):
# if pos_scores[(r, c)] == np.inf:
# continue
if not utility.position_is_passable(
board, (r, c), enemies):
continue
deg_free = 0
for row, col in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
new_pos = (r + row, c + col)
if new_pos[0] < 0 or new_pos[0] > 10:
continue
if new_pos[1] < 0 or new_pos[1] > 10:
continue
if utility.position_is_passable(
board, new_pos,
enemies) or utility.position_is_flames(
board, new_pos):
deg_free += 1
deg_frees[(r, c)] = deg_free
if deg_free <= 1:
pos_scores[(r, c)] -= 5
if self.logging:
print('deg_free')
print(deg_frees)
# consider bomb blast
for i in range(len(bombs)):
r, c = bombs[i]['position']
strength = bombs[i]['blast_strength']
pos_scores[(r, c)] = -20
for row, col in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
for d in range(1, strength):
new_pos = (r + d * row, c + d * col)
if new_pos[0] < 0 or new_pos[0] > 10:
continue
if new_pos[1] < 0 or new_pos[1] > 10:
continue
if new_pos not in dist:
continue
elif new_pos == np.inf:
continue
pos_scores[new_pos] = -20
if self.logging:
print('consider blast pos_score:')
print(pos_scores)
# consider enemies
for e in enemies:
pos = items.get(e, [])
if len(pos) > 0:
r = pos[0][0]
c = pos[0][1]
for row, col in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
for d in range(1, blast_strength * 2):
new_pos = (r + d * row, c + d * col)
if new_pos[0] < 0 or new_pos[0] > 10:
continue
if new_pos[1] < 0 or new_pos[1] > 10:
continue
if not utility.position_is_passable(
board, new_pos, enemies):
break
pos_scores[new_pos] += 0.3
if self.logging:
print('consider enemy:')
print(pos_scores)
h_r, h_c = -1, -1
h_score = -1
for c in range(11):
for r in range(11):
if (r, c) not in dist:
continue
elif dist[(r, c)] == np.inf:
continue
if h_score < pos_scores[(r, c)]:
h_score = pos_scores[(r, c)]
h_r, h_c = (r, c)
if self.logging:
print('h_score and pos:', h_score, '(r, c) =', h_r, ',', h_c)
print('prev:')
print(prev)
# if current position is not the highest score position, move to the highest position.
if h_r == -1:
# print('action: Stop')
self.prev_action = constants.Action.Stop
# return constants.Action.Stop.value
elif pos_scores[my_position] == h_score:
if self._can_escape(pos_scores, my_position, blast_strength):
# print('set bomb')
self.prev_action = constants.Action.Bomb
# return constants.Action.Bomb.value
else:
# print('action: Stop2')
self.prev_action = constants.Action.Stop
# return constants.Action.Stop.value
else:
# print('action: backtrack')
self.prev_action = self._backtrack(my_position, (h_r, h_c), prev)
# return self._backtrack(my_position, (h_r, h_c), prev)
self.prev_pos = my_position
if self.logging:
print('action: ', self.prev_action)
return self.prev_action.value
# Move if we are in an unsafe place.
unsafe_directions = self._directions_in_range_of_bomb(
board, my_position, bombs, dist)
if unsafe_directions:
directions = self._find_safe_directions(board, my_position,
unsafe_directions, bombs,
enemies)
return random.choice(directions).value
# Lay pomme if we are adjacent to an enemy.
if self._is_adjacent_enemy(items, dist, enemies) and self._maybe_bomb(
ammo, blast_strength, items, dist, my_position):
return constants.Action.Bomb.value
# Move towards an enemy if there is one in exactly three reachable spaces.
direction = self._near_enemy(my_position, items, dist, prev, enemies,
3)
if direction is not None and (self._prev_direction != direction
or random.random() < .5):
self._prev_direction = direction
return direction.value
# Move towards a good item if there is one within two reachable spaces.
direction = self._near_good_powerup(my_position, items, dist, prev, 2)
if direction is not None:
return direction.value
# Maybe lay a bomb if we are within a space of a wooden wall.
if self._near_wood(my_position, items, dist, prev, 1):
if self._maybe_bomb(ammo, blast_strength, items, dist,
my_position):
return constants.Action.Bomb.value
else:
return constants.Action.Stop.value
# Move towards a wooden wall if there is one within two reachable spaces and you have a bomb.
direction = self._near_wood(my_position, items, dist, prev, 2)
if direction is not None:
directions = self._filter_unsafe_directions(
board, my_position, [direction], bombs)
if directions:
return directions[0].value
# Choose a random but valid direction.
directions = [
constants.Action.Stop, constants.Action.Left,
constants.Action.Right, constants.Action.Up, constants.Action.Down
]
valid_directions = self._filter_invalid_directions(
board, my_position, directions, enemies)
directions = self._filter_unsafe_directions(board, my_position,
valid_directions, bombs)
directions = self._filter_recently_visited(
directions, my_position, self._recently_visited_positions)
if len(directions) > 1:
directions = [k for k in directions if k != constants.Action.Stop]
if not len(directions):
directions = [constants.Action.Stop]
# Add this position to the recently visited uninteresting positions so we don't return immediately.
self._recently_visited_positions.append(my_position)
self._recently_visited_positions = self._recently_visited_positions[
-self._recently_visited_length:]
return random.choice(directions).value
def act(self, obs, action_space):
#
# Definitions
#
self._search_range = 10
board = obs['board']
my_position = obs["position"] # tuple([x,y]): my position
my_ammo = obs['ammo'] # int: the number of bombs I have
my_blast_strength = obs['blast_strength']
my_enemies = [constants.Item(e) for e in obs['enemies']]
#
# Prepare extended observations
# - bomb moving direction
# - flame remaining life
#
# Summarize information about bombs
# curr_bombs : list of current bombs
# moving_direction : array of moving direction of bombs
curr_bombs, moving_direction, self._prev_bomb_life \
= self._get_bombs(obs, self._prev_bomb_life)
# Summarize information about flames
curr_flames, self._prev_flame_life \
= self._get_flames(board, self._prev_flame_life, self._prev_bomb_position_strength)
# bombs to be exploded in the next step
self._prev_bomb_position_strength = list()
rows, cols = np.where(obs["bomb_blast_strength"] > 0)
for position in zip(rows, cols):
strength = int(obs["bomb_blast_strength"][position])
self._prev_bomb_position_strength.append((position, strength))
#
# Understand current situation
#
# Simulation assuming enemies stay unmoved
# List of simulated boards
list_boards_no_move, _ \
= self._board_sequence(board,
curr_bombs,
curr_flames,
self._search_range,
my_position,
enemy_mobility=0)
# List of the set of survivable time-positions at each time
# and preceding positions
survivable_no_move, prev_no_move \
= self._search_time_expanded_network(list_boards_no_move,
my_position)
# Items that can be reached in a survivable manner
reachable_items_no_move, reached_no_move, next_to_items_no_move \
= self._find_reachable_items(list_boards_no_move,
my_position,
survivable_no_move)
# Simulation assuming enemies move
for enemy_mobility in range(3, -1, -1):
# List of boards simulated
list_boards, _ = self._board_sequence(board,
curr_bombs,
curr_flames,
self._search_range,
my_position,
enemy_mobility=enemy_mobility)
# List of the set of survivable time-positions at each time
# and preceding positions
survivable, prev = self._search_time_expanded_network(list_boards,
my_position)
if len(survivable[1]) > 0:
# Gradually reduce the mobility of enemy, so we have at least one survivable action
break
# Items that can be reached in a survivable manner
reachable_items, reached, next_to_items \
= self._find_reachable_items(list_boards,
my_position,
survivable)
# Survivable actions
is_survivable, survivable_with_bomb \
= self._get_survivable_actions(survivable,
obs,
curr_bombs,
curr_flames)
survivable_actions = [a for a in is_survivable if is_survivable[a]]
if verbose:
print("survivable actions are", survivable_actions)
# Positions where we kick a bomb if we move to
kickable = self._kickable_positions(obs, moving_direction)
print()
for t in range(0):
print(list_boards[t])
print(survivable[t])
for key in prev[t]:
print(key, prev[t][key])
#
# Choose an action
#
"""
# This is not effective in the current form
if len(survivable_actions) > 1:
# avoid the position if only one position at the following step
# the number of positions that can be reached from the next position
next = defaultdict(set)
next_count = defaultdict(int)
for position in survivable[1]:
next[position] = set([p for p in prev[2] if position in prev[2][p]])
next_count[position] = len(next[position])
print("next count", next_count)
if max(next_count.values()) > 1:
for position in survivable[1]:
if next_count[position] == 1:
risky_action = self._get_direction(my_position, position)
is_survivable[risky_action] = False
survivable_actions = [a for a in is_survivable if is_survivable[a]]
"""
# Do not stay on a bomb if I can
if all([obs["bomb_life"][my_position] > 0,
len(survivable_actions) > 1,
is_survivable[constants.Action.Stop]]):
is_survivable[constants.Action.Stop] = False
survivable_actions = [a for a in is_survivable if is_survivable[a]]
if len(survivable_actions) == 0:
# must die
# TODO: might want to do something that can help team mate
# TODO: kick if possible
print("Must die", constants.Action.Stop)
return super().act(obs, action_space)
# return constants.Action.Stop.value
elif len(survivable_actions) == 1:
# move to the position if it is the only survivable position
action = survivable_actions[0]
print("The only survivable action", action)
return action.value
# Move towards good items
good_items = [constants.Item.ExtraBomb, constants.Item.IncrRange]
# TODO : kick may be a good item only if I cannot kick yet
# TODO : might want to destroy
good_items.append(constants.Item.Kick)
# positions with good items
good_time_positions = set()
for item in good_items:
good_time_positions = good_time_positions.union(reachable_items[item])
if len(good_time_positions) > 0:
action = self._find_distance_minimizer(my_position,
good_time_positions,
prev,
is_survivable)
if action is not None:
print("Moving toward good item", action)
return action.value
# TODO : shoud check the survivability of all agents in one method
# Place a bomb if
# - it does not significantly reduce my survivability
# - it can break wood
# - it can reduce the survivability of enemies
if is_survivable[constants.Action.Bomb]:
# if survavable now after bomb, consider bomb
if all([len(s) > 0 for s in survivable_with_bomb]):
# if survivable all the time after bomb, consider bomb
if all([self._can_break_wood(list_boards_no_move[-1],
my_position,
my_blast_strength)]
+ [not utility.position_is_flames(board, my_position)
for board in list_boards_no_move[:10]]):
# place bomb if can break wood
print("Bomb to break wood", constants.Action.Bomb)
return constants.Action.Bomb.value
for enemy in my_enemies:
# check if the enemy is reachable
if len(reachable_items_no_move[enemy]) == 0:
continue
# can reach the enemy at enemy_position in enemy_time step
enemy_time = reachable_items_no_move[enemy][0][0]
enemy_position = reachable_items_no_move[enemy][0][1:3]
# find direction towards enemy
positions = set([x[1:3] for x in next_to_items_no_move[enemy]])
for t in range(enemy_time, 1, -1):
_positions = set()
for position in positions:
_positions = _positions.union(prev_no_move[t][position])
positions = _positions.copy()
#if enemy_time <= my_blast_strength:
if True:
positions.add(my_position)
positions_after_bomb = set(survivable[1]).difference(positions)
if positions_after_bomb:
print("Bomb to kill an enemy", enemy, constants.Action.Bomb)
return constants.Action.Bomb.value
else:
# bomb to kick
x0, y0 = my_position
positions_against = [(2*x0-x, 2*y0-y) for (x, y) in positions]
positions_after_bomb = set(survivable[1]).intersection(positions_against)
if positions_after_bomb:
print("Bomb to kick", enemy, constants.Action.Bomb)
return constants.Action.Bomb.value
"""
# check if placing a bomb can reduce the survivability
# of the enemy
survivable_before, _ = self._search_time_expanded_network(list_boards_no_move,
enemy_position)
board_with_bomb = deepcopy(obs["board"])
curr_bombs_with_bomb = deepcopy(curr_bombs)
# lay a bomb
board_with_bomb[my_position] = constants.Item.Bomb.value
bomb = characters.Bomb(characters.Bomber(), # dummy owner of the bomb
my_position,
constants.DEFAULT_BOMB_LIFE,
my_blast_strength,
None)
curr_bombs_with_bomb.append(bomb)
list_boards_with_bomb, _ \
= self._board_sequence(board_with_bomb,
curr_bombs_with_bomb,
curr_flames,
self._search_range,
my_position,
enemy_mobility=0)
survivable_after, _ \
= self._search_time_expanded_network(list_boards_with_bomb,
enemy_position)
good_before = np.array([len(s) for s in survivable_before])
good_after = np.array([len(s) for s in survivable_after])
# TODO : what are good criteria?
if any(good_after < good_before):
# place a bomb if it makes sense
print("Bomb to kill an enemy", constants.Action.Bomb)
return constants.Action.Bomb.value
"""
# Move towards a wood
if len(next_to_items_no_move[constants.Item.Wood]) > 0:
# positions next to wood
good_time_positions = next_to_items_no_move[constants.Item.Wood]
action = self._find_distance_minimizer(my_position,
good_time_positions,
prev,
is_survivable)
if action is not None:
print("Moving toward wood", action)
return action.value
# kick whatever I can kick
# -- tentative, this is generally not a good strategy
if len(kickable) > 0:
while kickable:
# then consider what happens if I kick a bomb
next_position = kickable.pop()
# do not kick a bomb if it will break a wall
if all([moving_direction[next_position] is None,
self._can_break_wood(board, next_position, my_blast_strength)]):
# if it is a static bomb
# do not kick if it is breaking a wall
continue
my_action = self._get_direction(my_position, next_position)
list_boards_with_kick, next_position \
= self._board_sequence(obs["board"],
curr_bombs,
curr_flames,
self._search_range,
my_position,
my_action=my_action,
can_kick=True,
enemy_mobility=3)
survivable_with_kick, prev_kick \
= self._search_time_expanded_network(list_boards_with_kick[1:],
next_position)
if next_position in survivable_with_kick[0]:
print("Kicking", my_action)
return my_action.value
# Move towards an enemy
good_time_positions = set()
for enemy in my_enemies:
good_time_positions = good_time_positions.union(next_to_items[enemy])
if len(good_time_positions) > 0:
action = self._find_distance_minimizer(my_position,
good_time_positions,
prev,
is_survivable)
if obs["bomb_life"][my_position] > 0:
# if on a bomb, move away
if action == constants.Action.Down and is_survivable[constants.Action.Up]:
action = constants.Action.Up
elif action == constants.Action.Up and is_survivable[constants.Action.Down]:
action = constants.Action.Down
elif action == constants.Action.Right and is_survivable[constants.Action.Left]:
action = constants.Action.Left
elif action == constants.Action.Left and is_survivable[constants.Action.Right]:
action = constants.Action.Right
else:
action = None
if action is not None:
print("Moving toward/against enemy", action)
return action.value
#
# as in the agent from the previous competition
#
action = super().act(obs, action_space)
if is_survivable[constants.Action(action)]:
print("Action from prev. agent", constants.Action(action))
return action
else:
action = random.choice(survivable_actions)
print("Random action", action)
return action.value
def _djikstra(board,
my_position,
bombs,
enemies,
depth=None,
exclude=None):
"""
Dijkstra method
Parameters
----------
board = np.array(obs['board'])
my_position = tuple(obs['position'])
bombs = convert_bombs(np.array(obs['bomb_blast_strength']))
enemies = [constants.Item(e) for e in obs['enemies']]
"""
if depth is None:
depth = len(board) * 2
if exclude is None:
exclude = [
constants.Item.Fog, constants.Item.Rigid, constants.Item.Flames
]
def out_of_range(p1, p2):
x1, y1 = p1
x2, y2 = p2
return abs(y2 - y1) + abs(x2 - x1) > depth
items = defaultdict(list)
for bomb in bombs:
if bomb['position'] == my_position:
items[constants.Item.Bomb].append(my_position)
dist = {}
prev = {}
mx, my = my_position
for r in range(max(0, mx - depth), min(len(board), mx + depth)):
for c in range(max(0, my - depth), min(len(board), my + depth)):
position = (r, c)
if any([
out_of_range(my_position, position),
utility.position_in_items(board, position, exclude),
]):
continue
if position == my_position:
dist[position] = 0
else:
dist[position] = np.inf
prev[position] = None
item = constants.Item(board[position])
items[item].append(position)
# Djikstra
H = []
heapq.heappush(H, (0, my_position))
while H:
min_dist, position = heapq.heappop(H)
if not utility.position_is_passable(board, position, enemies):
continue
x, y = position
for row, col in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
new_position = (row + x, col + y)
if new_position not in dist:
continue
if min_dist + 1 < dist[new_position]:
dist[new_position] = min_dist + 1
prev[new_position] = position
heapq.heappush(H, (dist[new_position], new_position))
return items, dist, prev
def act(self, obs, action_space):
def convert_bombs(bomb_map):
ret = []
locations = np.where(bomb_map > 0)
for r, c in zip(locations[0], locations[1]):
ret.append({
'position': (r, c),
'blast_strength': int(bomb_map[(r, c)])
})
return ret
my_position = tuple(obs['position'])
board = np.array(obs['board'])
bombs = convert_bombs(np.array(obs['bomb_blast_strength']))
enemies = [constants.Item(e) for e in obs['enemies']]
ammo = int(obs['ammo'])
blast_strength = int(obs['blast_strength'])
items, dist, prev = self._djikstra(board,
my_position,
bombs,
enemies,
depth=20)
# safety score
safety_score = self._make_safety_score(board, items, bombs, enemies)
if safety_score[my_position] == -np.inf:
max_distance = 5
safe_positions = list()
maybe_positions = list()
mx, my = my_position
for x in range(max([0, mx - max_distance]),
min([len(board), mx + max_distance])):
for y in range(max([0, my - max_distance]),
min([len(board), my + max_distance])):
if not (x, y) in dist:
# unreachable
continue
if safety_score[(x, y)] > 1:
safe_positions.append((x, y))
elif safety_score[(x, y)] == 1:
maybe_positions.append((x, y))
nearest = None
dist_to = max(dist.values())
for position in safe_positions:
d = dist[position]
if d <= dist_to:
nearest = position
dist_to = d
if dist_to > max_distance:
for position in maybe_positions:
d = dist[position]
if d <= dist_to:
nearest = position
dist_to = d
if nearest is not None:
# found a way to escape
if prev[nearest] is not None:
direction = self._get_direction_towards_position(
my_position, nearest, prev)
if verbose:
print("escaping")
return direction.value
# Move towards a powerup
direction = self._near_good_powerup(my_position, items, dist, prev, 20)
if direction is not None:
if safety_score[utility.get_next_position(my_position,
direction)] > 1:
if verbose:
print("item")
return direction.value
else:
if verbose:
print("item but unsafe")
# Break whatever you can break
to_break = self._what_to_break(board, my_position, blast_strength)
maybe = self._maybe_bomb(ammo, blast_strength, items, dist,
my_position)
if len(to_break) > 0 and maybe:
if verbose:
print("to break", to_break)
return constants.Action.Bomb.value
# Move towards an enemy
if constants.Item.Wood not in items and constants.Item.ExtraBomb not in items and constants.Item.Kick not in items:
to_chase = 3
else:
to_chase = 3
direction = self._near_enemy(my_position, items, dist, prev, enemies,
to_chase)
if direction is not None and (self._prev_direction != direction
or random.random() < .5):
self._prev_direction = direction
if verbose:
print("enemy")
return direction.value
# Move towards a wooden wall if there is one within two reachable spaces and you have a bomb.
direction = self._near_wood(my_position, items, dist, prev, 2)
if direction is not None:
if safety_score[utility.get_next_position(my_position,
direction)] > 1:
if verbose:
print("wood")
return direction.value
# Choose a random but valid direction.
directions = [
constants.Action.Stop, constants.Action.Left,
constants.Action.Right, constants.Action.Up, constants.Action.Down
]
valid_directions = self._filter_invalid_directions(
board, my_position, directions, enemies)
directions = self._filter_unsafe_directions(board, my_position,
valid_directions, bombs)
directions = self._filter_recently_visited(
directions, my_position, self._recently_visited_positions)
if len(directions) > 1:
directions = [k for k in directions if k != constants.Action.Stop]
if not len(directions):
directions = [constants.Action.Stop]
# Add this position to the recently visited uninteresting positions so we don't return immediately.
self._recently_visited_positions.append(my_position)
self._recently_visited_positions = self._recently_visited_positions[
-self._recently_visited_length:]
p = [
safety_score[utility.get_next_position(my_position, d)]
for d in directions
]
if verbose:
print("random", p, directions)
p = np.exp(p)
if len(p) == 1:
p = [1]
else:
p /= np.sum(p)
try:
#return np.random.choice(directions, p=p).value
return random.choices(directions, weights=p).value
except:
return random.choice(directions).value
def act(self, obs, action_space, info):
#
# Definitions
#
enemy_mobility = 4
enemy_bomb = 1
board = obs['board']
my_position = obs["position"] # tuple([x,y]): my position
my_ammo = obs['ammo'] # int: the number of bombs I have
my_blast_strength = obs['blast_strength']
my_enemies = [constants.Item(e) for e in obs['enemies']]
my_teammate = obs["teammate"]
my_kick = obs["can_kick"] # whether I can kick
print("my position",
my_position,
"ammo",
my_ammo,
"blast",
my_blast_strength,
"kick",
my_kick,
end="\t")
#
# Understand current situation
#
# fraction of blocked node in the survival trees of enemies
_list_boards = deepcopy(info["list_boards_no_move"])
if obs["bomb_blast_strength"][my_position]:
for b in _list_boards:
if utility.position_is_agent(b, my_position):
b[my_position] = constants.Item.Bomb.value
else:
for b in _list_boards:
if utility.position_is_agent(b, my_position):
b[my_position] = constants.Item.Passage.value
total_frac_blocked, n_survivable_nodes \
= self._get_frac_blocked(_list_boards, my_enemies, board, obs["bomb_life"])
#np.set_printoptions(precision=2)
#print("frac")
#print(total_frac_blocked)
# where to place bombs to break wood
bomb_target_wood, n_breakable \
= self._get_bomb_target(info["list_boards_no_move"][-1],
my_position,
my_blast_strength,
constants.Item.Wood,
max_breakable=False)
#bomb_target_enemy = (total_frac_blocked > 0)
#bomb_target = bomb_target_enemy + bomb_target_wood
bomb_target = bomb_target_wood
# List of boards simulated
list_boards, _ = self._board_sequence(
board,
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
enemy_mobility=enemy_mobility,
enemy_bomb=enemy_bomb,
enemy_blast_strength=info["enemy_blast_strength"])
# some bombs may explode with extra bombs, leading to under estimation
for t in range(len(list_boards)):
flame_positions = np.where(
info["list_boards_no_move"][t] == constants.Item.Flames.value)
list_boards[t][flame_positions] = constants.Item.Flames.value
#print("boards")
#for t, b in enumerate(list_boards):
# print(t)
# print(b[-3:,:])
# if t > 2:
# break
# List of the set of survivable time-positions at each time
# and preceding positions
survivable, prev, succ, _ \
= self._search_time_expanded_network(list_boards,
my_position)
# Survivable actions
is_survivable, survivable_with_bomb \
= self._get_survivable_actions(survivable,
obs,
info["curr_bombs"],
info["curr_flames"],
enemy_mobility=enemy_mobility,
enemy_bomb=enemy_bomb,
enemy_blast_strength=info["enemy_blast_strength"])
n_survivable = dict()
kick_actions = list()
if my_kick:
# Positions where we kick a bomb if we move to
kickable, _ = self._kickable_positions(obs,
info["moving_direction"])
for next_position in kickable:
# consider what happens if I kick a bomb
my_action = self._get_direction(my_position, next_position)
# do not kick into fog
dx = next_position[0] - my_position[0]
dy = next_position[1] - my_position[1]
position = next_position
is_fog = False
while self._on_board(position):
if utility.position_is_fog(board, position):
is_fog = True
break
position = (position[0] + dx, position[1] + dy)
if is_fog:
continue
list_boards_with_kick, next_position \
= self._board_sequence(obs["board"],
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
my_action=my_action,
can_kick=True,
enemy_mobility=enemy_mobility,
enemy_bomb=enemy_bomb,
enemy_blast_strength=info["enemy_blast_strength"])
#print(list_boards_with_kick)
survivable_with_kick, prev_kick, succ_kick, _ \
= self._search_time_expanded_network(list_boards_with_kick[1:],
next_position)
if next_position in survivable_with_kick[0]:
is_survivable[my_action] = True
n_survivable[my_action] = [1] + [
len(s) for s in survivable_with_kick[1:]
]
kick_actions.append(my_action)
else:
kickable = set()
survivable_actions = [a for a in is_survivable if is_survivable[a]]
#print("survivable actions", survivable_actions)
if len(survivable_actions) == 0:
return None
#
# Items and bomb target that can be reached in a survivable manner
#
reachable_items, reached, next_to_items \
= self._find_reachable_items(list_boards,
my_position,
survivable,
bomb_target)
#
# Evaluate the survivability of each action
#
x, y = my_position
for action in survivable_actions:
# for each survivable action, check the survivability
if action == constants.Action.Bomb:
n_survivable[action] = [
len(s) for s in survivable_with_bomb[1:]
]
continue
if action == constants.Action.Up:
dx = -1
dy = 0
elif action == constants.Action.Down:
dx = 1
dy = 0
elif action == constants.Action.Left:
dx = 0
dy = -1
elif action == constants.Action.Right:
dx = 0
dy = 1
elif action == constants.Action.Stop:
dx = 0
dy = 0
else:
raise ValueError()
next_position = (x + dx, y + dy)
n_survivable[action], _info = self._count_survivable(
succ, 1, next_position)
if verbose:
print("n_survivable")
for a in n_survivable:
print(a, n_survivable[a])
#
# Avoid the action leading to no choice if possible
#
updated = False
max_survivable_positions = max([n[-1] for n in n_survivable.values()])
if max_survivable_positions > 1:
for a in n_survivable:
if n_survivable[a][-1] > max_survivable_positions / 2:
continue
is_survivable[a] = False
updated = True
minn = defaultdict(int)
for a in n_survivable:
minn[a] = min(n_survivable[a][enemy_mobility:])
maxmin = max(minn.values())
if maxmin > 1:
for a in minn:
if minn[a] == 1:
is_survivable[a] = False
updated = True
if updated:
survivable_actions = [a for a in is_survivable if is_survivable[a]]
#
# Choose the survivable action, if it is the only choice
#
if len(survivable_actions) == 1:
# move to the position if it is the only survivable position
action = survivable_actions[0]
print("The only survivable action", action)
return action.value
"""
#
# Bomb if it has dominating survivability
#
if is_survivable[constants.Action.Bomb]:
bomb_is_most_survivable = True
bomb_sorted = np.array(sorted(n_survivable[constants.Action.Bomb]))
for action in n_survivable:
if action == constants.Action.Bomb:
continue
action_sorted = np.array(sorted(n_survivable[action]))
if any(action_sorted > bomb_sorted):
bomb_is_most_survivable = False
break
if bomb_is_most_survivable:
action = constants.Action.Bomb
print("Bomb to survive", action)
return action.value
"""
#
# Bomb at a target
#
best_action = None
max_block = 0
for action in survivable_actions:
next_position = self._get_next_position(my_position, action)
block = total_frac_blocked[next_position]
if block > max_block:
max_block = block
best_action = action
if all([
is_survivable[constants.Action.Bomb], best_action
in [constants.Action.Stop, constants.Action.Bomb]
]):
print("Place a bomb at a locally optimal position",
constants.Action.Bomb)
return constants.Action.Bomb.value
#
# Move towards where to bomb
#
if best_action not in [None, constants.Action.Bomb]:
next_position = self._get_next_position(my_position, best_action)
# TODO : PARAMETER TO OPTIMIZE
if total_frac_blocked[next_position] > 0.1:
print("Move towards better place to bomb", best_action)
return best_action.value
#
# Bomb to break wood
#
consider_bomb = True
if survivable_with_bomb is None:
consider_bomb = False
elif any([len(s) <= 1 for s in survivable_with_bomb[2:]]):
# if not sufficiently survivable all the time after bomb, do not bomb
consider_bomb = False
elif self._might_break_powerup(info["list_boards_no_move"][-1],
my_position, my_blast_strength,
info["might_powerup"]):
# if might break an item, do not bomb
consider_bomb = False
if consider_bomb and bomb_target[my_position]:
# place bomb if I am at a bomb target
print("Bomb at a bomb target", constants.Action.Bomb)
return constants.Action.Bomb.value
#
# Move towards good items
#
good_items = [
constants.Item.ExtraBomb, constants.Item.IncrRange,
constants.Item.Kick
]
good_time_positions = set() # positions with good items
for item in good_items:
good_time_positions = good_time_positions.union(
reachable_items[item])
if len(good_time_positions) > 0:
action = self._find_distance_minimizer(my_position,
good_time_positions, prev,
is_survivable)
if action is not None:
print("Moving toward good item", action)
return action.value
#
# Move towards where to bomb to break wood
#
good_time_positions = reachable_items["target"]
print("good time positions", good_time_positions)
action = self._find_distance_minimizer(my_position,
good_time_positions, prev,
is_survivable)
if action is not None:
print("Moving toward where to bomb", action)
return action.value
#
# Kick
#
for my_action in kick_actions:
if my_action == constants.Action.Up:
next_position = (my_position[0] - 1, my_position[1])
elif my_action == constants.Action.Down:
next_position = (my_position[0] + 1, my_position[1])
elif my_action == constants.Action.Right:
next_position = (my_position[0], my_position[1] + 1)
elif my_action == constants.Action.Left:
next_position = (my_position[0], my_position[1] - 1)
# do not kick a bomb if it will break a wall, enemies
if info["moving_direction"][next_position] is None:
print("checking static bomb")
# if it is a static bomb
if self._can_break(info["list_boards_no_move"][0],
next_position, my_blast_strength,
[constants.Item.Wood] + my_enemies):
continue
list_boards_with_kick_no_move, _ \
= self._board_sequence(obs["board"],
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
my_action=my_action,
can_kick=True,
enemy_mobility=0)
for enemy in my_enemies:
rows, cols = np.where(board == enemy.value)
if len(rows) == 0:
continue
enemy_position = (rows[0], cols[0])
_survivable, _, _, _ \
= self._search_time_expanded_network(list_boards_with_kick_no_move,
enemy_position)
n_survivable_nodes_with_kick = sum(
[len(positions) for positions in _survivable])
if n_survivable_nodes_with_kick < n_survivable_nodes[enemy]:
print("Kicking to reduce the survivability",
n_survivable_nodes[enemy], "->",
n_survivable_nodes_with_kick, my_action)
return my_action.value
#
# TODO : move toward might powerups
#
#
# Move towards a fog where we have not seen longest
#
best_time_position = None
oldest = 0
for t, x, y in next_to_items[constants.Item.Fog]:
neighbors = [(x + dx, y + dy)
for dx, dy in [(-1, 0), (1, 0), (0, -1), (0, 1)]]
age = max([
info["since_last_seen"][position] for position in neighbors
if self._on_board(position)
])
if age > oldest:
oldest = age
best_time_position = (t, x, y)
if best_time_position is not None:
action = self._find_distance_minimizer(my_position,
[best_time_position], prev,
is_survivable)
if action is not None:
print("Moving toward oldest fog", action)
return action.value
#
# Choose most survivable action
#
action = self._get_most_survivable_action(n_survivable)
print("Most survivable action", action)
return action.value
def act(self, obs, action_space, info):
#
# Definitions
#
board = info['recently_seen']
#board = obs['board']
my_position = obs["position"] # tuple([x,y]): my position
my_ammo = obs['ammo'] # int: the number of bombs I have
my_blast_strength = obs['blast_strength']
my_kick = obs["can_kick"] # whether I can kick
my_enemies = [
constants.Item(e) for e in obs['enemies']
if e != constants.Item.AgentDummy
]
if obs["teammate"] != constants.Item.AgentDummy:
my_teammate = obs["teammate"]
else:
my_teammate = None
all_feasible_actions = [
a for a in info["my_next_position"] if info["my_next_position"][a]
]
# positions that might be blocked
if info["teammate_position"] is None:
agent_positions = info["enemy_positions"]
else:
agent_positions = info["enemy_positions"] + [
info["teammate_position"]
]
#
# Fraction of blocked node in the survival trees of enemies
#
_list_boards = info["list_boards_no_move"]
if obs["bomb_blast_strength"][my_position]:
for b in _list_boards:
if utility.position_is_agent(b, my_position):
b[my_position] = constants.Item.Bomb.value
else:
for b in _list_boards:
if utility.position_is_agent(b, my_position):
b[my_position] = constants.Item.Passage.value
total_frac_blocked, n_survivable_nodes, blocked_time_positions \
= self._get_frac_blocked(_list_boards, my_enemies, board, obs["bomb_life"],
ignore_dying_agent=False)
if info["teammate_position"] is not None:
total_frac_blocked_teammate, n_survivable_nodes_teammate, blocked_time_positions_teammate \
= self._get_frac_blocked(_list_boards, [my_teammate], board, obs["bomb_life"],
ignore_dying_agent=True)
block = defaultdict(float)
for action in [
constants.Action.Stop, constants.Action.Up,
constants.Action.Down, constants.Action.Left,
constants.Action.Right
]:
next_position = info["my_next_position"][action]
if next_position is None:
continue
if next_position in info["all_kickable"]:
# kick will be considered later
continue
block[action] = total_frac_blocked[next_position]
if info["teammate_position"] is not None and block[action] > 0:
block[action] *= (1 -
total_frac_blocked_teammate[next_position])
if block[action] > 0:
block[action] *= self._inv_tmp
block[action] -= np.log(-np.log(self.random.uniform()))
if all([my_ammo > 0, obs["bomb_blast_strength"][my_position] == 0]):
list_boards_with_bomb, _ \
= self._board_sequence(board,
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
my_blast_strength=my_blast_strength,
my_action=constants.Action.Bomb,
step_to_collapse=info["step_to_collapse"],
collapse_ring=info["collapse_ring"])
block[constants.Action.Bomb] \
= self._get_frac_blocked_two_lists(list_boards_with_bomb,
n_survivable_nodes,
board,
my_enemies,
ignore_dying_agent=False)
block[constants.Action.Bomb] \
+= total_frac_blocked[my_position] * (1 - block[constants.Action.Bomb])
if info["teammate_position"] is not None:
block_teammate_with_bomb = self._get_frac_blocked_two_lists(
list_boards_with_bomb,
n_survivable_nodes_teammate,
board, [my_teammate],
ignore_dying_agent=True)
# this is an approximation
block_teammate_with_bomb \
+= total_frac_blocked_teammate[my_position] * (1 - block_teammate_with_bomb)
block[constants.Action.Bomb] *= (1 - block_teammate_with_bomb)
if block[constants.Action.Bomb] > 0:
block[constants.Action.Bomb] *= self._inv_tmp
block[constants.Action.Bomb] -= np.log(
-np.log(self.random.uniform()))
block_teammate_with_kick = defaultdict(float)
for next_position in info["all_kickable"]:
my_action = self._get_direction(my_position, next_position)
backedup = False
if board[next_position] != constants.Item.Bomb.value:
backup_cell = board[next_position]
board[
next_position] = constants.Item.Bomb.value # an agent will be overwritten
backedup = True
list_boards_with_kick, _ \
= self._board_sequence(board,
info["curr_bombs"],
info["curr_flames"],
self._search_range,
my_position,
my_action=my_action,
can_kick=True,
step_to_collapse=info["step_to_collapse"],
collapse_ring=info["collapse_ring"])
if backedup:
board[next_position] = backup_cell
block[my_action] \
= self._get_frac_blocked_two_lists(list_boards_with_kick,
n_survivable_nodes,
board,
my_enemies)
block[my_action] \
+= total_frac_blocked[next_position] * (1 - block[my_action])
if block[my_action] > 0 and info["teammate_position"] is not None:
block_teammate_with_kick[next_position] \
= self._get_frac_blocked_two_lists(list_boards_with_kick,
n_survivable_nodes_teammate,
board,
[my_teammate],
ignore_dying_agent=True)
# this is an approximation
block_teammate_with_kick[next_position] \
+= total_frac_blocked_teammate[next_position] * (1 - block_teammate_with_kick[next_position])
block[my_action] *= (1 -
block_teammate_with_kick[next_position])
if block[my_action] > 0:
block[my_action] *= self._inv_tmp
block[my_action] -= np.log(-np.log(self.random.uniform()))
n_survivable_move, is_survivable_move, list_boards_move \
= self._get_survivable(obs, info, my_position, info["my_next_position"], info["enemy_positions"],
info["all_kickable"], allow_kick_to_fog=True,
enemy_mobility=1, enemy_bomb=0,
ignore_dying_agent=False,
step_to_collapse=info["step_to_collapse"],
collapse_ring=info["collapse_ring"])
for a in all_feasible_actions:
if a not in n_survivable_move:
n_survivable_move[a] = np.zeros(self._search_range)
enemy_can_place_bomb = any([
obs["bomb_blast_strength"][position] == 0
for position in info["enemy_positions"]
])
if enemy_can_place_bomb:
n_survivable_bomb, is_survivable_bomb, list_boards_bomb \
= self._get_survivable(obs, info, my_position, info["my_next_position"], info["enemy_positions"],
info["all_kickable"], allow_kick_to_fog=True,
enemy_mobility=0, enemy_bomb=1,
ignore_dying_agent=False,
step_to_collapse=info["step_to_collapse"],
collapse_ring=info["collapse_ring"])
for a in all_feasible_actions:
if a not in n_survivable_bomb:
n_survivable_bomb[a] = np.zeros(self._search_range)
might_survivable_actions = set(
[a
for a in n_survivable_bomb if n_survivable_bomb[a][-1] > 0] +
[a for a in n_survivable_move if n_survivable_move[a][-1] > 0])
might_survivable_actions -= info["might_block_actions"]
for a in info["might_block_actions"]:
n_survivable_bomb[a] = np.zeros(self._search_range)
n_survivable_move[a] = np.zeros(self._search_range)
for a in might_survivable_actions:
if a not in n_survivable_bomb:
n_survivable_bomb[a] = np.zeros(self._search_range)
if a not in n_survivable_move:
n_survivable_move[a] = np.zeros(self._search_range)
survivable_actions = list()
for action in might_survivable_actions:
if n_survivable_move[action][-1] > 0 and n_survivable_bomb[
action][-1] > 0:
if not info["might_blocked"][action] or n_survivable_move[
constants.Action.Stop][-1] > 0:
survivable_actions.append(action)
n_survivable_expected = dict()
for a in survivable_actions:
if info["might_blocked"][a]:
n_survivable_expected[a] \
= np.array(n_survivable_bomb[a]) \
+ np.array(n_survivable_move[constants.Action.Stop]) \
+ np.array(n_survivable_move[a])
n_survivable_expected[a] = n_survivable_expected[a] / 3
else:
n_survivable_expected[a] = np.array(
n_survivable_bomb[a]) + 2 * np.array(
n_survivable_move[a])
n_survivable_expected[a] = n_survivable_expected[a] / 3
n_survivable_expected[a] = n_survivable_expected[a]
else:
might_survivable_actions = set(
[a for a in n_survivable_move if n_survivable_move[a][-1] > 0])
might_survivable_actions -= info["might_block_actions"]
for a in info["might_block_actions"]:
n_survivable_move[a] = np.zeros(self._search_range)
survivable_actions = list()
for action in might_survivable_actions:
if n_survivable_move[action][-1] > 0:
if not info["might_blocked"][action] or n_survivable_move[
constants.Action.Stop][-1] > 0:
survivable_actions.append(action)
for a in might_survivable_actions:
if a not in n_survivable_move:
n_survivable_move[a] = np.zeros(self._search_range)
n_survivable_expected = dict()
for a in survivable_actions:
if info["might_blocked"][a]:
n_survivable_expected[a] \
= np.array(n_survivable_move[constants.Action.Stop]) \
+ np.array(n_survivable_move[a])
n_survivable_expected[a] = n_survivable_expected[a] / 2
else:
n_survivable_expected[a] = np.array(n_survivable_move[a])
#
# Choose actions
#
if len(survivable_actions) == 1:
action = survivable_actions.pop()
return action.value
if len(survivable_actions) > 1:
most_survivable_actions = self._get_most_survivable_actions(
n_survivable_expected)
if len(most_survivable_actions) == 1:
return most_survivable_actions[0].value
elif len(most_survivable_actions) > 1:
# tie break by block score
max_block = 0 # do not choose 0
best_action = None
for action in all_feasible_actions:
if action not in most_survivable_actions:
# for deterministic behavior
continue
if info["might_block_teammate"][action]:
continue
if block[action] > max_block:
max_block = block[action]
best_action = action
if best_action is not None:
return best_action.value
#
# no survivable actions for all cases
#
if enemy_can_place_bomb:
n_survivable_expected = dict()
for a in all_feasible_actions:
if info["might_blocked"][a]:
if is_survivable_move[constants.Action.Stop]:
n_survivable_expected[a] \
= np.array(n_survivable_bomb[a]) \
+ np.array(n_survivable_move[constants.Action.Stop]) \
+ np.array(n_survivable_move[a])
else:
n_survivable_expected[a] \
= np.array(n_survivable_bomb[a]) \
+ np.array(n_survivable_move[a])
n_survivable_expected[a] = n_survivable_expected[a] / 3
else:
n_survivable_expected[a] = np.array(
n_survivable_bomb[a]) + 2 * np.array(
n_survivable_move[a])
n_survivable_expected[a] = n_survivable_expected[a] / 3
else:
n_survivable_expected = dict()
for a in all_feasible_actions:
if info["my_next_position"][a] is None:
continue
if info["might_blocked"][a]:
if is_survivable_move[constants.Action.Stop]:
n_survivable_expected[a] \
= np.array(n_survivable_move[constants.Action.Stop]) \
+ np.array(n_survivable_move[a])
else:
n_survivable_expected[a] = np.array(
n_survivable_move[a])
n_survivable_expected[a] = n_survivable_expected[a] / 2
else:
n_survivable_expected[a] = np.array(n_survivable_move[a])
if len(might_survivable_actions) == 1:
action = might_survivable_actions.pop()
return action.value
if len(might_survivable_actions) > 1:
most_survivable_actions = self._get_most_survivable_actions(
n_survivable_expected)
if len(most_survivable_actions) == 1:
return most_survivable_actions[0].value
elif len(most_survivable_actions) > 1:
# tie break by block score
max_block = 0 # do not choose 0
best_action = None
for action in all_feasible_actions:
if action not in most_survivable_actions:
# for deterministic behavior
continue
if info["might_block_teammate"][action]:
continue
if block[action] > max_block:
max_block = block[action]
best_action = action
if best_action is not None:
return best_action.value
# no survivable action found for any cases
# TODO : Then consider killing enemies or helping teammate
max_block = 0 # do not choose 0
best_action = None
for action in all_feasible_actions:
if action not in block:
# for deterministic behavior
continue
if info["might_block_teammate"][action]:
continue
if all([
action == constants.Action.Bomb, info["teammate_position"]
is not None
]):
if block_teammate_with_bomb > 0:
continue
next_position = info["my_next_position"][action]
if all([
next_position in info["all_kickable"],
block_teammate_with_kick[next_position] > 0
]):
continue
if block[action] > max_block:
max_block = block[action]
best_action = action
if best_action is not None:
return best_action.value
# longest survivable action
longest_survivable_actions = self._get_longest_survivable_actions(
n_survivable_expected)
if len(longest_survivable_actions) == 1:
return longest_survivable_actions[0].value
elif len(longest_survivable_actions) > 1:
# break tie by most survivable actions
for a in n_survivable_expected:
if a not in longest_survivable_actions:
n_survivable_expected[a] = np.zeros(self._search_range)
most_survivable_actions = self._get_most_survivable_actions(
n_survivable_expected)
if len(most_survivable_actions) == 1:
return most_survivable_actions[0].value
elif len(most_survivable_actions) > 1:
if info["teammate_position"] is not None:
min_block = np.inf
best_action = None
for a in all_feasible_actions:
if a not in most_survivable_actions:
# for deterministic behavior
continue
if a == constants.Action.Bomb:
score = block_teammate_with_bomb # do not choose Bomb unless it is strictly better than others
else:
next_position = info["my_next_position"][a]
if next_position in info["all_kickable"]:
score = block_teammate_with_kick[
next_position] - self.random.uniform(
0, 1e-6)
else:
score = total_frac_blocked_teammate[
next_position] - self.random.uniform(
0, 1e-6)
if score < min_block:
min_block = score
best_action = a
if best_action is not None:
return best_action.value
else:
# remove Bomb (as it is most affected by bugs)
#most_survivable_actions = list(set(most_survivable_actions) - {constants.Action.Bomb})
most_survivable_actions = [
a for a in all_feasible_actions
if a in most_survivable_actions
and a != constants.Action.Bomb
]
index = self.random.randint(len(most_survivable_actions))
random_action = most_survivable_actions[index]
return random_action.value
# The following will not be used
self.random.shuffle(all_feasible_actions)
if len(all_feasible_actions):
action = all_feasible_actions[0]
return action.value
action = constants.Action.Stop
return action.value
def act(self, obs, action_space):
if self.prev_ammo is None:
self.prev_ammo = self.ammo
self.prev_blast_strength = self.blast_strength
self.prev_can_kick = self.can_kick
self.prev_wood_wall = np.sum(obs['board'] == 2)
self.steps += 1
state = self.process_observation(obs['board'], obs['position'],
obs['bomb_life'],
obs['bomb_blast_strength'],
obs['enemies'])
self.memory.store(self.prev_state, state, 0, self.prev_action)
self.prev_state = state
self.prev_ammo = self.ammo
self.prev_blast_strength = self.blast_strength
self.prev_can_kick = self.can_kick
self.prev_wood_wall = np.sum(obs['board'] == 2)
def convert_bombs(bomb_map):
'''Flatten outs the bomb array'''
ret = []
locations = np.where(bomb_map > 0)
for r, c in zip(locations[0], locations[1]):
ret.append({
'position': (r, c),
'blast_strength': int(bomb_map[(r, c)])
})
return ret
my_position = tuple(obs['position'])
board = np.array(obs['board'])
bombs = convert_bombs(np.array(obs['bomb_blast_strength']))
enemies = [constants.Item(e) for e in obs['enemies']]
ammo = int(obs['ammo'])
blast_strength = int(obs['blast_strength'])
items, dist, prev = self._djikstra(board,
my_position,
bombs,
enemies,
depth=10)
# Move if we are in an unsafe place.
unsafe_directions = self._directions_in_range_of_bomb(
board, my_position, bombs, dist)
if unsafe_directions:
directions = self._find_safe_directions(board, my_position,
unsafe_directions, bombs,
enemies)
action = random.choice(directions).value
self.prev_action = action
return action
# Lay pomme if we are adjacent to an enemy.
if self._is_adjacent_enemy(items, dist, enemies) and self._maybe_bomb(
ammo, blast_strength, items, dist, my_position):
action = constants.Action.Bomb.value
self.prev_action = action
return action
# Move towards an enemy if there is one in exactly three reachable spaces.
direction = self._near_enemy(my_position, items, dist, prev, enemies,
3)
if direction is not None and (self._prev_direction != direction
or random.random() < .5):
self._prev_direction = direction
action = direction.value
self.prev_action = action
return action
# Move towards a good item if there is one within two reachable spaces.
direction = self._near_good_powerup(my_position, items, dist, prev, 2)
if direction is not None:
action = direction.value
self.prev_action = action
return action
# Maybe lay a bomb if we are within a space of a wooden wall.
if self._near_wood(my_position, items, dist, prev, 1):
if self._maybe_bomb(ammo, blast_strength, items, dist,
my_position):
action = constants.Action.Bomb.value
self.prev_action = action
return action
else:
action = constants.Action.Stop.value
self.prev_action = action
return action
# Move towards a wooden wall if there is one within two reachable spaces and you have a bomb.
direction = self._near_wood(my_position, items, dist, prev, 2)
if direction is not None:
directions = self._filter_unsafe_directions(
board, my_position, [direction], bombs)
if directions:
action = directions[0].value
self.prev_action = action
return action
# Choose a random but valid direction.
directions = [
constants.Action.Stop, constants.Action.Left,
constants.Action.Right, constants.Action.Up, constants.Action.Down
]
valid_directions = self._filter_invalid_directions(
board, my_position, directions, enemies)
directions = self._filter_unsafe_directions(board, my_position,
valid_directions, bombs)
directions = self._filter_recently_visited(
directions, my_position, self._recently_visited_positions)
if len(directions) > 1:
directions = [k for k in directions if k != constants.Action.Stop]
if not len(directions):
directions = [constants.Action.Stop]
# Add this position to the recently visited uninteresting positions so we don't return immediately.
self._recently_visited_positions.append(my_position)
self._recently_visited_positions = self._recently_visited_positions[
-self._recently_visited_length:]
action = random.choice(directions).value
self.prev_action = action
return action
def act(self, obs, action_space):
self.action_space = action_space
my_pos = tuple(obs['position'])
board = np.array(obs['board'])
self.board = np.array(obs['board'])
self._enemies = [constants.Item(e) for e in obs['enemies']]
if (self.copy_walls):
for i in range(len(self.copy_board)):
for j in range(len(self.copy_board[i])):
if (board[i][j] == 1):
self.copy_board[i][j] = 9999
self.copy_board[my_pos[0]][my_pos[1]] += 1
#check new bombs on field first
bomb_life_map = np.array(obs['bomb_life'])
self.find_bombing_agents(bomb_life_map, board)
#print("bomb_life_map \n", bomb_life_map)
#preform MCTS ONLY IF ENEMY AGENT IS VISISIBLE
if self.enemy_in_my_sights_and_ammo(obs, 5):
#print("my ammo",int(obs['ammo']))
#print("HELLO MCTS")
#check board, to see if someone inside my view made a bomb move
tree = gn.Tree(obs, True, self.bombing_agents)
#get the root node
self.rootNode = tree.get_root_node()
#need way to find terminating condition
self.end_time = 30
start_time = time.time()
elapsed = 0
#while(elapsed < self.end_time):
while (self.rootNode.visit_count < 25):
#print("board \n",self.rootNode.state._board)
#for i in self.rootNode.childArray:
#print("ROOT NODES CHILDREN")
#print("i move is",i.state.move)
#print("i temp score is", i.state.score)
#print("i temp node score is", i.score)
promising_node = self.select_promising_node(self.rootNode)
#print("promisng nodes move", promising_node.my_move)
#print("promising nodes score", promising_node.score)
#expand that node
#create the childs for that node
self.expand_node(promising_node)
# print("EXPANDED PROMISE NODE")
# for i in promising_node.childArray:
# print("PROMISING NODES CHILDREN")
# print("i move is",i.state.move)
# print("i temp score is", i.state.score)
# print("i temp node score is", i.score)
# #explore that node
# print("LENGTH OF CHILDREN", len(promising_node.childArray))
nodeToExplore = promising_node.get_random_child_node()
#print("Node to explore", nodeToExplore.state.move)
#simulate
simulationResult = self.simulate_random_play(nodeToExplore)
# simulationResult = self.simulate_random_play_yichen(nodeToExplore, self.copy_board)
#propogate up
self.back_propogation(nodeToExplore, simulationResult)
nowTime = time.time()
elapsed += (nowTime - start_time)
start_time = nowTime
# input()
#winner is root node with child with big score
#winner_node = rootNode.get_child_with_max_score()
winner_node = None
max_ucb = float('-inf')
for child in self.rootNode.childArray:
#print("child move is", child.state.move)
#print ("the node", child.score)
#print ("the node's state", child.state.score)
UCB1 = self.UCB(child, child.get_win_score(),
child.get_visit_count(),
self.rootNode.get_visit_count())
if UCB1 > max_ucb:
max_ucb = UCB1
winner_node = child
#print("winning childs move is ", winner_node.state.move)
#print("winning childs score is", winner_node.score)
#print("UCB is", UCB1)
self.bombing_agents = winner_node.state.bombing_agents
#print("the move I make is",winner_node.state.move)
#print("the move I picked score", winner_node.score)
return winner_node.state.move
#yichen agent time
else:
#print("YICHEN AGENT TIME")
self.agt = agents.YichenAgent()
self.agt.make_a_visit_board(self.copy_board)
aid = board[my_pos[0]][my_pos[1]]
game_mode = constants.GameType.FFA
position = my_pos
self.agt.init_agent(aid, game_mode)
self.agt.set_start_position(position)
self.agt.reset(is_alive=True)
r = self.agt.act(obs, action_space)
#print (r)
return r
