def step(self, move=None):
"""
Perform one step of the environment: agents in turn choose a move
:param move: externally determined move to be performed by agent0 (useful for training)
:return: reward accumulated in this step, boolean: if environment in terminal state, boolean: if agent0 won
"""
self.reward = 0
self.steps += 1 # illegal move as step
# are there still pieces to be moved?
if not helpers.get_poss_moves(self.board, team=0):
self.reward += self.reward_loss
self.score += self.reward
return self.reward, True, -2 # 0 for lost
# move decided by agent or externally?
if move is not None:
agent_move = move # this enables working with the environment in external functions (e.g. train.py)
else:
agent_move = self.agents[0].decide_move()
# is move legal?
if not helpers.is_legal_move(
self.board, agent_move
): # if illegal -> no change in env, receive reward_illegal
self.reward += self.reward_illegal
self.illegal_moves += 1
# print("Warning: agent 1 selected an illegal move: {}".format(agent_move))
self.score += self.reward
done, won = self.goal_test()
return self.reward, done, won # environment does not change for illegal
self.do_move(agent_move, team=0)
self.move_count += 1
# opponents move
if self.opp_can_move: # only if opponent is playing, killing his pieces wins (opponent can be e.g. flag only)
# are there still pieces to be moved?
if not helpers.get_poss_moves(self.board, team=1):
self.reward += self.reward_win
self.score = self.reward
return self.reward, True, 2 # 1 for won
opp_move = self.agents[1].decide_move()
# is move legal?
if not helpers.is_legal_move(
self.board, opp_move
): # opponent is assumed to only perform legal moves
pass
# print("Warning: agent 1 selected an illegal move: {}".format(opp_move))
self.do_move(opp_move,
team=1) # assuming only legal moves selected
self.move_count += 1
done, won = self.goal_test()
self.score += self.reward
return self.reward, done, -1 + 2 * won
def min_val(self, board, current_reward, alpha, beta, depth):
"""
Step of the minimizing player in the minimax algorithm. See max_val for documentation.
"""
# this is what the opponent will think, the min-player
# get my possible actions, then shuffle them to ensure randomness when no action
# stands out as the best
my_doable_actions = helpers.get_poss_moves(board, self.other_team)
np.random.shuffle(my_doable_actions)
# check for terminal-state scenario or maximum depth
done, won = self.goal_test(my_doable_actions, board, max_val=False)
if done or depth == 0:
return current_reward + self.get_terminal_reward(done, won, depth), None
val = float('inf') # initial value set, so min comparison later possible
best_action = None
# iterate through all actions
for action in my_doable_actions:
board, fight_result = self.do_move(action, board=board, bookkeeping=False, true_gameplay=False)
temp_reward = current_reward - self.add_temp_reward(fight_result)
new_val = self.max_val(board, temp_reward, alpha, beta, depth-1)[0]
if val > new_val:
val = new_val
best_action = action
if val <= alpha:
self.undo_last_move(board)
return val, best_action
beta = min(beta, val)
board = self.undo_last_move(board)
return val, best_action
def poss_actions(self, action_dim):
"""
Converting set of possible moves in the whole game to a set of actions for the agent
:param action_dim: how many actions are possible for agent
:return: list of legal actions
"""
poss_moves = helpers.get_poss_moves(self.board, self.team) # which moves are possible in the game
poss_actions = []
all_actions = range(0, action_dim)
for action in all_actions:
move = self.action_to_move(action) # converting all actions to moves (which can be illegal)
if move in poss_moves: # only select legal moves among them
poss_actions.append(action)
return poss_actions
def decide_move(self):
"""
given the maximum depth, copy the known board so far, assign the pieces by random, while still
respecting the current knowledge, and then decide the move via minimax algorithm.
:return: tuple of position tuples
"""
possible_moves = helpers.get_poss_moves(self.board, self.team)
next_action = None
if possible_moves:
values_of_moves = dict.fromkeys(possible_moves, 0)
for move in possible_moves:
for draw in range(self._nr_of_enemy_setups_to_draw):
curr_board = self.draw_consistent_enemy_setup(copy.deepcopy(self.board))
curr_board, _ = self.do_move(move, curr_board, bookkeeping=False, true_gameplay=False)
values_of_moves[move] += self.approximate_value_of_board(curr_board) / self._nr_of_enemy_setups_to_draw
self.undo_last_move(curr_board)
evaluations = list(values_of_moves.values())
actions = list(values_of_moves.keys())
next_action = actions[evaluations.index(max(evaluations))]
return next_action
def max_val(self, board, current_reward, alpha, beta, depth):
"""
Do the max players step in the minimax algorithm. Check first if the given board is in
a terminal state. If not, we will do each possible move once and send the process to
min_val to do the min players step.
:param board: the current board, numpy array
:param current_reward: the current value the path has accumulated
:param alpha: alpha threshold of the minimax alg
:param beta: beta threshold of the minimax alg
:param depth: the depth the process is at, integer
:return: tuple of best value, and a associated best_action (float, tuple)
"""
# this is what the expectimax agent will think
# get my possible actions, then shuffle them to ensure randomness when no action
# stands out as the best
my_doable_actions = helpers.get_poss_moves(board, self.team)
np.random.shuffle(my_doable_actions)
# check for terminal-state scenario
done, won = self.goal_test(my_doable_actions, board, max_val=True)
if done or depth == 0:
return current_reward + self.get_terminal_reward(done, won, depth), None
val = -float('inf')
best_action = None
for action in my_doable_actions:
board, fight_result = self.do_move(action, board=board, bookkeeping=False, true_gameplay=False)
temp_reward = current_reward + self.add_temp_reward(fight_result)
new_val = self.min_val(board, temp_reward, alpha, beta, depth-1)[0]
if val < new_val:
val = new_val
best_action = action
if val >= beta:
self.undo_last_move(board)
best_action = action
return val, best_action
alpha = max(alpha, val)
board = self.undo_last_move(board)
return val, best_action
def approximate_value_of_board(self, board):
"""
Simulate the game to the max number of turns a lot of times and evaluating the simulation
by whether he won and how many more pieces he has left than the opponent.
:param board:
:return:
"""
finished = False
turn = 0
evals = []
for i in range(self._nr_iterations_of_game_sim):
board_copy = copy.deepcopy(board)
while not finished:
actions = helpers.get_poss_moves(board_copy, turn)
if actions: # as long as actions are left to be done, we do them
move = random.choice(actions)
board_copy, _ = self.do_move(move, board_copy)
# check whether the game is terminal
done, won = self.goal_test(actions, board_copy, turn)
if done:
# if terminal, calculate the bonus we want to reward this simulation with
my_team = self.get_team_from_board(board, self.team)
enemy_team = self.get_team_from_board(board, self.other_team)
bonus = (len(my_team) - len(enemy_team)) / 20
# -1+2*won equals -1+2*0=-1 for won=False, and -1+2*1=1 for won=True
# bonus is negative if enemy team has more pieces
evals.append(-1 + 2 * won + bonus)
finished = True
elif turn > self._nr_of_max_turn_sim: # check if we reached the max number of turns
# calculate bonus
my_team = self.get_team_from_board(board, self.team)
enemy_team = self.get_team_from_board(board, self.other_team)
bonus = (len(my_team) - len(enemy_team)) / 20
# -1+2*won equals -1+2*0=-1 for won=False, and -1+2*1=1 for won=True
# bonus is negative if enemy team has more pieces
evals.append(bonus)
finished = True
turn = (turn + 1) % 2
return sum(evals)/len(evals)
def decide_move(self):
actions = helpers.get_poss_moves(self.board, self.team)
if not actions:
return None
else:
return random.choice(actions)
