def test(train_ep: int, num_episodes: int, game_interface: HFOAttackingPlayer,
features: DiscreteHighLevelFeatures, agent: QLearningAgent,
actions: ActionManager):
# Run training using Q-Learning
score = 0
agent.test_episodes.append(train_ep)
for ep in range(num_episodes):
print('<Test> {}/{}:'.format(ep, num_episodes))
while game_interface.in_game():
# Update environment features:
observation = game_interface.get_state()
curr_state_id = features.get_state_index(observation)
has_ball = features.has_ball(observation)
# Act:
action_idx = agent.exploit_actions(curr_state_id)
hfo_action = actions.map_action(action_idx)
# Step:
status, observation = game_interface.step(hfo_action, has_ball)
# Save Metrics:
agent.save_visited_state(curr_state_id, action_idx)
agent.cum_reward += reward_function(status)
print(':: Episode: {}; reward: {}'.format(ep, agent.cum_reward))
score += 1 if game_interface.status == GOAL else 0
# Reset player:
agent.reset(training=False)
# Game Reset
game_interface.reset()
agent.scores.append(score)
actions_name = [
actions_manager.map_action_to_str(i) for i in range(agent.num_actions)
]
agent.export_metrics(training=False, actions_name=actions_name)
def __init__(self,
num_opponents: int,
num_teammates: int,
port: int = 6000,
online: bool = True):
# Game Interface:
self.game_interface = HFOAttackingPlayer(num_opponents=num_opponents,
num_teammates=num_teammates,
port=port)
if online:
self.game_interface.connect_to_server()
# Features Interface:
self.features = PlasticFeatures(num_op=num_opponents,
num_team=num_teammates)
# Actions Interface:
self.actions = Actions()
# Agent instance:
self.agent = DQNAgent(num_features=self.features.num_features,
num_actions=self.actions.get_num_actions(),
learning_rate=0.005,
discount_factor=0.99,
epsilon=1,
final_epsilon=0.001,
epsilon_decay=0.99995,
tau=0.125)
def move_agent(self, action_name, game_interface: HFOAttackingPlayer,
features: BaseHighLevelState):
""" Agent Moves/Dribbles in a specific direction """
# Get Movement type:
action = DRIBBLE_TO
if "UP" in action_name:
action = (action, features.agent.x_pos, -0.9)
elif "DOWN" in action_name:
action = (action, features.agent.x_pos, 0.9)
elif "LEFT" in action_name:
action = (action, -0.8, features.agent.y_pos)
elif "RIGHT" in action_name:
action = (action, 0.8, features.agent.y_pos)
else:
raise ValueError("ACTION NAME is WRONG")
attempts = 0
while game_interface.in_game() and attempts < self.action_num_episodes:
status, observation = game_interface.step(action,
features.has_ball())
features.update_features(observation)
attempts += 1
return game_interface.get_game_status(), \
game_interface.get_observation_array()
def move_agent(action_name, game_interface: HFOAttackingPlayer,
features: DiscreteFeatures1TeammateV1):
# print("move_agent!")
if "SHORT" in action_name:
num_repetitions = 10
elif "LONG" in action_name:
num_repetitions = 20
else:
raise ValueError("ACTION NAME is WRONG")
# Get Movement type:
if "MOVE" in action_name:
action = MOVE_TO
elif "DRIBBLE" in action_name:
action = DRIBBLE_TO
else:
raise ValueError("ACTION NAME is WRONG")
if "UP" in action_name:
action = (action, features.agent_coord[0], -0.9)
elif "DOWN" in action_name:
action = (action, features.agent_coord[0], 0.9)
elif "LEFT" in action_name:
action = (action, -0.8, features.agent_coord[1])
elif "RIGHT" in action_name:
action = (action, 0.8, features.agent_coord[1])
else:
raise ValueError("ACTION NAME is WRONG")
attempts = 0
while game_interface.in_game() and attempts < num_repetitions:
status, observation = game_interface.step(action, features.has_ball())
features.update_features(observation)
attempts += 1
return status, observation
def test(num_episodes: int, game_interface: HFOAttackingPlayer,
features: DiscreteFeatures, agent: QLearningAgentTest,
actions: DiscreteActionsV5, reward_funct) -> float:
"""
@param num_episodes: number of episodes to run
@param game_interface: game interface, that manages interactions
between both;
@param features: features interface, from the observation array, gets the
main features for the agent;
@param agent: learning agent;
@param actions: actions interface;
@param reward_funct: reward function used
@return: (float) the average reward
"""
# Run training using Q-Learning
sum_score = 0
for ep in range(num_episodes):
# Check if server still up:
if game_interface.hfo.step() == SERVER_DOWN:
raise ServerDownError("testing; episode={}".format(ep))
# Go to origin position:
features.update_features(game_interface.get_state())
go_to_origin_position(game_interface=game_interface,
features=features,
actions=actions)
# Test loop:
debug_counter = 0 # TODO remove
while game_interface.in_game():
# Update environment features:
curr_state_id = features.get_state_index()
has_ball = features.has_ball()
if not has_ball:
hfo_action_params = GO_TO_BALL
num_rep = 5
else:
# Act:
debug_counter += 1
action_idx = agent.exploit_actions(curr_state_id)
hfo_action_params, num_rep = \
actions.map_action_idx_to_hfo_action(
agent_pos=features.get_pos_tuple(), has_ball=has_ball,
action_idx=action_idx)
# Step:
status, observation = execute_action(
action_params=hfo_action_params,
repetitions=num_rep,
has_ball=has_ball,
game_interface=game_interface)
# update features:
reward = reward_funct(status)
features.update_features(observation)
sum_score += reward
# Game Reset
game_interface.reset()
print("## AVR Test reward = ", sum_score / num_episodes)
return sum_score / num_episodes
def execute_action(action_params: tuple, repetitions: int,
game_interface: HFOAttackingPlayer, has_ball: bool):
rep_counter_aux = 0
observation = []
while game_interface.in_game() and rep_counter_aux < repetitions:
status, observation = game_interface.step(action_params, has_ball)
rep_counter_aux += 1
return game_interface.get_game_status(), observation
def test(num_episodes: int, game_interface: HFOAttackingPlayer,
features: DiscreteFeatures1TeammateV1, agent: QLearningAgent,
actions: DiscreteActions1TeammateV1, reward_funct) -> float:
"""
@param num_episodes: number of episodes to run
@param game_interface: game interface, that manages interactions
between both;
@param features: features interface, from the observation array, gets the
main features for the agent;
@param agent: learning agent;
@param actions: actions interface;
@param reward_funct: reward function used
@return: (float) the win rate
"""
# Run training using Q-Learning
num_goals = 0
for ep in range(num_episodes):
# Check if server still up:
if game_interface.hfo.step() == SERVER_DOWN:
print("Server is down while testing; episode={}".format(ep))
break
# Go to origin position:
features.update_features(game_interface.get_state())
go_to_origin_position(game_interface=game_interface,
features=features,
actions=actions)
# Test loop:
debug_counter = 0 # TODO remove
while game_interface.in_game():
# Update environment features:
curr_state_id = features.get_state_index()
has_ball = features.has_ball()
# Act:
debug_counter += 1
action_idx = agent.act(curr_state_id)
action_name = actions.map_action_to_str(action_idx, has_ball)
print("Agent playing {}".format(action_name))
# Step:
status = execute_action(action_name=action_name,
features=features,
game_interface=game_interface)
# update features:
reward = reward_funct(status)
num_goals += 1 if reward == 1 else 0
if status == OUT_OF_TIME:
if debug_counter < 5:
raise NoActionPlayedError(
"agent was only able to choose {}".format(debug_counter))
# Game Reset
game_interface.reset()
print("<<TEST>> NUM Goals = ", num_goals)
print("<<TEST>> NUM episodes = ", (ep + 1))
print("<<TEST>> AVR win rate = ", num_goals / (ep + 1))
return num_goals / num_episodes
def train(num_episodes: int, game_interface: HFOAttackingPlayer,
features: discrete_features_v2.DiscreteFeaturesV2,
agent: QLearningAgent, actions: DiscreteActions, reward_funct):
for ep in range(num_episodes):
print('<Training> Episode {}/{}:'.format(ep, num_episodes))
aux_positions_names = set()
aux_actions_played = set()
while game_interface.in_game():
# Update environment features:
features.update_features(game_interface.get_state())
curr_state_id = features.get_state_index()
has_ball = features.has_ball()
# Act:
action_idx = agent.act(curr_state_id)
aux_actions_played.add(actions.map_action_to_str(action_idx))
hfo_action: tuple = actions.map_action_idx_to_hfo_action(
features.get_pos_tuple(), action_idx)
# Step:
status, observation = game_interface.step(hfo_action, has_ball)
reward = reward_funct(status)
# Save metrics:
agent.save_visited_state(curr_state_id, action_idx)
agent.cum_reward += reward
aux_positions_names.add(features.get_position_name())
# Update environment features:
prev_state_id = curr_state_id
features.update_features(observation)
curr_state_id = features.get_state_index()
# Update agent
agent.learn(prev_state_id, action_idx, reward, status,
curr_state_id)
print(':: Episode: {}; reward: {}; positions: {}; actions: {}'.format(
ep, agent.cum_reward, aux_positions_names, aux_actions_played))
agent.save_metrics(agent.old_q_table, agent.q_table)
# Reset player:
agent.reset()
agent.update_hyper_parameters()
# Game Reset
game_interface.reset()
agent.save_model()
actions_name = [
actions_manager.map_action_to_str(i) for i in range(agent.num_actions)
]
agent.export_metrics(training=True, actions_name=actions_name)
def go_to_origin_position(game_interface: HFOAttackingPlayer,
features: DiscreteFeatures1TeammateV1,
actions: DiscreteActions1TeammateV1,
pos_name: str = None):
if pos_name:
origin_pos = ORIGIN_POSITIONS[pos_name]
else:
pos_name, origin_pos = random.choice(list(ORIGIN_POSITIONS.items()))
# print("\nMoving to starting point: {0}".format(pos_name))
pos = features.get_pos_tuple(round_ndigits=1)
while origin_pos != pos:
has_ball = features.has_ball()
hfo_action: tuple = actions.dribble_to_pos(origin_pos)
status, observation = game_interface.step(hfo_action, has_ball)
features.update_features(observation)
pos = features.get_pos_tuple(round_ndigits=1)
# Informs the teammate that it is ready to start the game
teammate_last_coord = features.teammate_coord.copy()
counter = 0
while teammate_last_coord.tolist() == features.teammate_coord.tolist():
if counter >= 10:
# print("STOP repeating the message")
break
game_interface.hfo.say(settings.PLAYER_READY_MSG)
game_interface.hfo.step()
observation = game_interface.hfo.getState()
features.update_features(observation)
# print("Action said READY!")
counter += 1
def __init__(self, num_opponents: int, num_teammates: int,
port: int = 6000):
# Game Interface:
self.game_interface = HFOAttackingPlayer(num_opponents=num_opponents,
num_teammates=num_teammates,
port=port)
self.game_interface.connect_to_server()
# Features Interface:
self.features = DiscFeatures1Teammate(num_op=num_opponents,
num_team=num_teammates)
# Actions Interface:
self.actions = Actions()
# Agent instance:
self.agent = QAgent(num_features=self.features.num_features,
num_actions=self.actions.get_num_actions(),
learning_rate=0.1, discount_factor=0.9, epsilon=0.8)
def kick_to_pos(self, pos: tuple, features: BaseHighLevelState,
game_interface: HFOAttackingPlayer):
""" The agent kicks to position expected """
hfo_action = (KICK_TO, pos[0], pos[1], 2)
status, observation = game_interface.step(hfo_action,
features.has_ball())
# Update features:
features.update_features(observation)
def pass_ball(game_interface: HFOAttackingPlayer,
features: DiscreteFeatures1TeammateV1):
# print("pass_ball!")
attempts = 0
while game_interface.in_game() and features.has_ball():
if attempts > 2:
break
elif attempts == 2:
# Failed to pass 2 times
print("Failed to PASS two times. WILL KICK")
y = random.choice([0.17, 0, -0.17])
hfo_action = (KICK_TO, 0.9, y, 2)
else:
hfo_action = (PASS, 11)
status, observation = game_interface.step(hfo_action,
features.has_ball())
features.update_features(observation)
attempts += 1
return status, observation
def shoot_ball(game_interface: HFOAttackingPlayer,
features: DiscreteFeatures1TeammateV1):
# print("shoot_ball!")
attempts = 0
while game_interface.in_game() and features.has_ball():
if attempts > 3:
break
elif attempts == 3:
# Failed to kick four times
print("Failed to SHOOT 3 times. WILL KICK")
y = random.choice([0.17, 0, -0.17])
hfo_action = (KICK_TO, 0.9, y, 2)
else:
hfo_action = (SHOOT, )
status, observation = game_interface.step(hfo_action,
features.has_ball())
features.update_features(observation)
attempts += 1
return status, observation
def shoot_ball(self, game_interface: HFOAttackingPlayer,
features: DiscreteFeatures1Teammate):
""" Tries to shoot, if it fail, kicks to goal randomly """
attempts = 0
while game_interface.in_game() and features.has_ball():
if attempts > 3:
break
elif attempts == 3:
# Failed to kick four times
# print("Failed to SHOOT 3 times. WILL KICK")
y = random.choice([0.17, 0, -0.17])
hfo_action = (KICK_TO, 0.9, y, 2)
else:
hfo_action = (SHOOT,)
_, obs = game_interface.step(hfo_action, features.has_ball())
features.update_features(obs)
attempts += 1
return game_interface.get_game_status(), \
game_interface.get_observation_array()
def train(num_episodes: int, game_interface: HFOAttackingPlayer,
features: DiscreteHighLevelFeatures, agent: QLearningAgent,
actions: ActionManager):
for ep in range(num_episodes):
print('<Training> Episode {}/{}:'.format(ep, num_episodes))
while game_interface.in_game():
# Update environment features:
observation = game_interface.get_state()
curr_state_id = features.get_state_index(observation)
has_ball = features.has_ball(observation)
# Act:
action_idx = agent.act(curr_state_id)
hfo_action = actions.map_action(action_idx)
# Step:
status, observation = game_interface.step(hfo_action, has_ball)
reward = reward_function(status)
# Save metrics:
agent.save_visited_state(curr_state_id, action_idx)
agent.cum_reward += reward
# Update environment features:
prev_state_id = curr_state_id
curr_state_id = features.get_state_index(observation)
# Update agent
agent.learn(prev_state_id, action_idx, reward, status,
curr_state_id)
print(':: Episode: {}; reward: {}'.format(ep, agent.cum_reward))
agent.save_metrics(agent.old_q_table, agent.q_table)
# Reset player:
agent.reset()
agent.update_hyper_parameters()
# Game Reset
game_interface.reset()
agent.save_model()
actions_name = [
actions_manager.map_action_to_str(i) for i in range(agent.num_actions)
]
agent.export_metrics(training=False, actions_name=actions_name)
def pass_ball(self, game_interface: HFOAttackingPlayer,
features: DiscreteFeatures1Teammate):
""" Tries to use the PASS action, if it fails, Kicks in the direction
of the teammate"""
attempts = 0
while game_interface.in_game() and features.has_ball():
if attempts > 2:
break
elif attempts == 2:
# Failed to pass 2 times
# print("Failed to PASS two times. WILL KICK")
y = random.choice([0.17, 0, -0.17])
hfo_action = (KICK_TO, 0.9, y, 2)
else:
hfo_action = (PASS, 11)
_, obs = game_interface.step(hfo_action, features.has_ball())
features.update_features(obs)
attempts += 1
return game_interface.get_game_status(), \
game_interface.get_observation_array()
def dribble_to_pos(self, pos: tuple, features: DiscreteFeatures1Teammate,
game_interface: HFOAttackingPlayer):
""" The agent keeps dribbling until reach the position expected """
curr_pos = features.get_pos_tuple(round_ndigits=1)
while pos != curr_pos:
hfo_action = (DRIBBLE_TO, pos[0], pos[1])
status, observation = game_interface.step(hfo_action,
features.has_ball())
# Update features:
features.update_features(observation)
curr_pos = features.get_pos_tuple(round_ndigits=1)
def move_to_pos(self, pos: tuple, features: BaseHighLevelState,
game_interface: HFOAttackingPlayer):
""" The agent keeps moving until reach the position expected """
curr_pos = features.get_pos_tuple(round_ndigits=1)
while pos != curr_pos:
hfo_action = (MOVE_TO, pos[0], pos[1])
status, observation = game_interface.step(hfo_action,
features.has_ball())
# Update features:
features.update_features(observation)
curr_pos = features.get_pos_tuple(round_ndigits=1)
def shoot_ball(self, game_interface: HFOAttackingPlayer,
features: BaseHighLevelState):
""" Tries to shoot, if it fail, kicks to goal randomly """
# Get best shoot angle:
angles = []
goalie_coord = np.array([features.opponents[0].x_pos,
features.opponents[0].y_pos])
player_coord = np.array(features.get_pos_tuple())
for goal_pos in self.shoot_possible_coord:
angles.append(get_angle(goalie=goalie_coord, player=player_coord,
point=goal_pos))
idx = int(np.argmax(np.array(angles)))
best_shoot_coord = self.shoot_possible_coord[idx]
# Action parameters:
hfo_action = (KICK_TO, best_shoot_coord[0], best_shoot_coord[1], 2.5)
# Step game:
_, obs = game_interface.step(hfo_action, features.has_ball())
# Update features:
features.update_features(obs)
return game_interface.get_game_status(), \
game_interface.get_observation_array()
def test(train_ep: int, num_episodes: int, game_interface: HFOAttackingPlayer,
features: discrete_features_v2.DiscreteFeaturesV2,
agent: QLearningAgentV4, actions: DiscreteActionsV2, reward_funct):
# Run training using Q-Learning
score = 0
agent.test_episodes.append(train_ep)
for ep in range(num_episodes):
print('<Test> {}/{}:'.format(ep, num_episodes))
prev_state_id =-1
while game_interface.in_game():
# Update environment features:
features.update_features(game_interface.get_state())
curr_state_id = features.get_state_index()
has_ball = features.has_ball()
# Act:
if prev_state_id != curr_state_id:
print([round(val, 2) for val in agent.q_table[curr_state_id]])
action_idx = agent.exploit_actions(curr_state_id)
hfo_action: tuple = actions.map_action_idx_to_hfo_action(
agent_pos=features.get_pos_tuple(), has_ball=has_ball,
action_idx=action_idx)
# Step:
status, observation = game_interface.step(hfo_action, has_ball)
prev_state_id = curr_state_id
# Save Metrics:
agent.save_visited_state(curr_state_id, action_idx)
agent.cum_reward += reward_funct(status)
print(':: Episode: {}; reward: {}'.format(ep, agent.cum_reward))
score += 1 if game_interface.status == GOAL else 0
# Reset player:
agent.reset(training=False)
# Game Reset
game_interface.reset()
agent.scores.append(score)
actions_name = [actions_manager.map_action_to_str(i, has_ball=True) for i in
range(agent.num_actions)]
agent.export_metrics(training=False, actions_name=actions_name)
def test(num_episodes: int, game_interface: HFOAttackingPlayer,
features: discrete_features_v2.DiscreteFeaturesV2,
agent: QLearningAgentV5, actions: DiscreteActionsV5, reward_funct):
"""
@param num_episodes: number of episodes to run
@param game_interface: game interface, that manages interactions
between both;
@param features: features interface, from the observation array, gets the
main features for the agent;
@param agent: learning agent;
@param actions: actions interface;
@param reward_funct: reward function used
@return: (int) the avarage reward
"""
# Run training using Q-Learning
sum_score = 0
for ep in range(num_episodes):
print('<Test> {}/{}:'.format(ep, num_episodes))
# Go to origin position:
features.update_features(game_interface.get_state())
go_to_origin_position(game_interface=game_interface,
features=features,
actions=actions)
# Test loop:
while game_interface.in_game():
# Update environment features:
curr_state_id = features.get_state_index()
has_ball = features.has_ball()
# Act:
action_idx = agent.exploit_actions(curr_state_id)
hfo_action_params, num_rep = \
actions.map_action_idx_to_hfo_action(
agent_pos=features.get_pos_tuple(), has_ball=has_ball,
action_idx=action_idx)
action_name = actions.map_action_to_str(action_idx, has_ball)
# Step:
rep_counter_aux = 0
while game_interface.in_game() and rep_counter_aux < num_rep:
status, observation = game_interface.step(
hfo_action_params, has_ball)
rep_counter_aux += 1
reward = reward_funct(status)
# update features:
features.update_features(observation)
# Save metrics:
agent.save_visited_state(curr_state_id, action_idx)
sum_score += reward
# Reset player:
agent.reset(training=False)
# Game Reset
game_interface.reset()
return sum_score / num_episodes
def go_to_origin_position(game_interface: HFOAttackingPlayer,
features: DiscreteFeatures,
actions: DiscreteActionsV5,
random_start: bool = True):
if random_start:
pos_name, origin_pos = random.choice(list(ORIGIN_POSITIONS.items()))
else:
pos_name = "Fixed start"
origin_pos = features.get_pos_tuple()
print("Moving to starting point: {0}".format(pos_name))
pos = features.get_pos_tuple(round_ndigits=1)
while origin_pos != pos:
has_ball = features.has_ball()
hfo_action: tuple = actions.dribble_to_pos(origin_pos)
status, observation = game_interface.step(hfo_action, has_ball)
features.update_features(observation)
pos = features.get_pos_tuple(round_ndigits=1)
def dribble_to_pos(self, pos: tuple, features: BaseHighLevelState,
game_interface: HFOAttackingPlayer):
""" The agent keeps dribbling until reach the position expected """
def check_valid_pos(pos_tuple: tuple):
for pos_aux in pos_tuple:
try:
num_digits = len(str(pos_aux).split(".")[1])
if num_digits >= 2:
return False
except IndexError:
pass
return True
if check_valid_pos(pos) is False:
raise Exception("Initial positions invalid. Initial positions "
"should be a float with 1 digit or less")
curr_pos = features.get_pos_tuple(round_ndigits=1)
while pos != curr_pos:
hfo_action = (DRIBBLE_TO, pos[0], pos[1])
status, observation = game_interface.step(hfo_action,
features.has_ball())
# Update features:
features.update_features(observation)
curr_pos = features.get_pos_tuple(round_ndigits=1)
def do_nothing(self, game_interface: HFOAttackingPlayer,
features: DiscreteFeatures1Teammate):
action = (NOOP,)
status, observation = game_interface.step(action, features.has_ball())
return status, observation
class Player:
def __init__(self, num_opponents: int, num_teammates: int):
# Game Interface:
self.game_interface = HFOAttackingPlayer(num_opponents=num_opponents,
num_teammates=num_teammates)
self.game_interface.connect_to_server()
# Features Interface:
self.features = DiscreteFeatures1Teammate(num_op=num_opponents,
num_team=num_teammates)
# Actions Interface:
self.actions = DiscreteActionsModule()
# Agent instance:
self.agent = QAgent(num_features=self.features.num_features,
num_actions=self.actions.get_num_actions(),
learning_rate=0.1,
discount_factor=0.9,
epsilon=1,
final_epsilon=0.3)
def get_reward(self, status: int) -> int:
return basic_reward(status)
def set_starting_game_conditions(self,
game_interface: HFOAttackingPlayer,
features: DiscreteFeatures1Teammate,
start_with_ball: bool = True,
start_pos: tuple = None):
"""
Set starting game conditions. Move for initial position, for example
"""
if not start_pos:
pos_name, start_pos = random.choice(
list(STARTING_POSITIONS.items()))
if start_with_ball:
# Move to starting position:
self.actions.dribble_to_pos(start_pos, features, game_interface)
else:
if self.features.has_ball():
self.actions.kick_to_pos((0, 0), features, game_interface)
# Move to starting position:
self.actions.move_to_pos(start_pos, features, game_interface)
# Informs the other players that it is ready to start:
game_interface.hfo.say(settings.PLAYER_READY_MSG)
def train(self,
num_train_episodes: int,
num_total_train_ep: int,
start_with_ball: bool = True):
"""
@param num_train_episodes: number of episodes to train in this iteration
@param num_total_train_ep: number total of episodes to train
@param start_with_ball: bool
@raise ServerDownError
@return: (QLearningAgentV5) the agent
"""
# metrics variables:
_num_wins = 0
_sum_epsilons = 0
for ep in range(num_train_episodes):
# Check if server still running:
self.game_interface.check_server_is_up()
# Update features:
self.features.update_features(self.game_interface.get_state())
# Go to origin position:
self.set_starting_game_conditions(
game_interface=self.game_interface,
features=self.features,
start_with_ball=start_with_ball)
# Start learning loop
goal = False # bool flag
while self.game_interface.in_game():
# Update environment features:
features_array = self.features.get_features().copy()
# Act:
action_idx = self.agent.act(features_array)
status = self.actions.execute_action(
action_idx=action_idx,
features=self.features,
game_interface=self.game_interface)
# Every step we update replay memory and train main network
done = not self.game_interface.in_game()
goal = self.game_interface.scored_goal()
self.agent.store_transition(
curr_st=features_array,
action_idx=action_idx,
reward=self.get_reward(status),
new_st=self.features.get_features(),
done=done)
# Train
self.agent.train(goal)
# Update auxiliar variables:
_sum_epsilons += self.agent.epsilon
_num_wins += 1 if self.game_interface.scored_goal() else 0
# Update Agent:
self.agent.restart(num_total_train_ep)
# Game Reset
self.game_interface.reset()
print("[TRAIN: Summary] WIN rate = {}; AVR epsilon = {}".format(
_num_wins / num_train_episodes,
_sum_epsilons / num_train_episodes))
def test(self,
num_episodes: int,
start_with_ball: bool = True,
training: bool = False) -> float:
"""
@param num_episodes: number of episodes to run
@param start_with_ball: flag
@param training: flag
@return: (float) the win rate
"""
starting_pos_list = list(STARTING_POSITIONS.values())
# metrics variables:
_num_wins = 0
for ep in range(num_episodes):
# Check if server still running:
self.game_interface.check_server_is_up()
# Update features:
self.features.update_features(self.game_interface.get_state())
# Set up gaming conditions:
self.set_starting_game_conditions(
game_interface=self.game_interface,
features=self.features,
start_pos=starting_pos_list[ep % len(starting_pos_list)],
start_with_ball=start_with_ball)
# Start learning loop
prev_action_idx = None
while self.game_interface.in_game():
# Update environment features:
features_array = self.features.get_features().copy()
# Act:
action_idx = self.agent.exploit_actions(features_array)
if prev_action_idx != action_idx and not training:
print("ACTION:: {}".format(
self.actions.map_action_to_str(
action_idx, self.features.has_ball())))
prev_action_idx = action_idx
self.actions.execute_action(action_idx=action_idx,
features=self.features,
game_interface=self.game_interface)
# Update auxiliar variables:
_num_wins += 1 if self.game_interface.scored_goal() else 0
# Game Reset
self.game_interface.reset()
avr_win_rate = _num_wins / num_episodes
print("[TEST: Summary] WIN rate = {};".format(avr_win_rate))
return avr_win_rate
def no_ball_action(self, game_interface: HFOAttackingPlayer,
features: BaseHighLevelState) -> int:
action = (MOVE, )
status, observation = game_interface.step(action, features.has_ball())
features.update_features(observation)
return status
def do_nothing(self, game_interface: HFOAttackingPlayer,
features: BaseHighLevelState):
action = (NOOP, )
status, observation = game_interface.step(action, features.has_ball())
return status, observation
parser.add_argument('--num_episodes', type=int, default=500)
parser.add_argument('--save_file', type=str, default=None)
args = parser.parse_args()
agent_id = args.id
num_team = args.num_teammates
num_op = args.num_opponents
num_episodes = args.num_episodes
saving_file = args.save_file
print("Starting Training - id={}; num_opponents={}; num_teammates={}; "
"num_episodes={}; saveFile={};".format(agent_id, num_op, num_team,
num_episodes, saving_file))
# Initialize connection with the HFO server
hfo_interface = HFOAttackingPlayer(agent_id=agent_id,
num_opponents=num_op,
num_teammates=num_team)
hfo_interface.connect_to_server()
# Reward Function
reward_function = simple_reward
# Get number of features and actions
features_manager = DiscreteHighLevelFeatures(num_team, num_op)
actions_manager = ActionManager([SHOOT, MOVE, DRIBBLE])
# Initialize a Q-Learning Agent
agent = QLearningAgent(num_states=features_manager.get_num_states(),
num_actions=actions_manager.get_num_actions(),
learning_rate=0.1,
discount_factor=0.99,
def train(num_train_episodes: int, num_total_train_ep: int,
game_interface: HFOAttackingPlayer,
features: DiscreteFeatures1TeammateV1, agent: QLearningAgent,
actions: DiscreteActions1TeammateV1, reward_funct):
"""
@param num_train_episodes: number of episodes to train in this iteration
@param num_total_train_ep: number total of episodes to train
@param game_interface: game interface, that manages interactions
between both;
@param features: features interface, from the observation array, gets
the main features for the agent;
@param agent: learning agent;
@param actions: actions interface;
@param reward_funct: reward function used
@return: (QLearningAgentV5) the agent
"""
sum_score = 0
sum_epsilons = 0
agent.counter_explorations = 0
agent.counter_exploitations = 0
for ep in range(num_train_episodes):
# Check if server still up:
if game_interface.hfo.step() == SERVER_DOWN:
raise ServerDownError("training; episode={}".format(ep))
# Go to origin position:
features.update_features(game_interface.get_state())
go_to_origin_position(game_interface=game_interface,
features=features,
actions=actions)
# Start learning loop
debug_counter = 0 # TODO remove
while game_interface.in_game():
# Update environment features:
curr_state_id = features.get_state_index()
has_ball = features.has_ball()
# Act:
debug_counter += 1
action_idx = agent.act(curr_state_id)
action_name = actions.map_action_to_str(action_idx, has_ball)
# print("Agent playing {} for {}".format(action_name, num_rep))
# Step:
status = execute_action(action_name=action_name,
features=features,
game_interface=game_interface)
# Update environment features:
reward = reward_funct(status)
sum_score += reward
new_state_id = features.get_state_index()
agent.store_ep(state_idx=curr_state_id,
action_idx=action_idx,
reward=reward,
next_state_idx=new_state_id,
has_ball=has_ball,
done=not game_interface.in_game())
if game_interface.get_game_status() == OUT_OF_TIME:
if debug_counter < 5:
raise NoActionPlayedError(
"agent was only able to choose {}".format(debug_counter))
agent.learn_buffer()
agent.update_hyper_parameters(num_total_episodes=num_total_train_ep)
sum_epsilons += agent.epsilon
# Game Reset
game_interface.reset()
print("<<TRAIN>> AVR reward = ", sum_score / num_train_episodes)
print("<<TRAIN>> %Explorations={}% ".format(
round(
(agent.counter_explorations /
(agent.counter_exploitations + agent.counter_explorations)), 4) *
100))
args = parser.parse_args()
num_team = args.num_teammates
num_op = args.num_opponents
num_train_ep = args.num_train_ep
num_test_ep = args.num_test_ep
num_repetitions = args.num_repetitions
num_episodes = (num_train_ep + num_test_ep) * num_repetitions
# Load Model
model_file = args.model_file
# Directory
save_dir = args.save_dir or mkdir(
num_episodes, num_op, extra_note="retrain")
# Initialize connection with the HFO server
hfo_interface = HFOAttackingPlayer(num_opponents=num_op,
num_teammates=num_team)
hfo_interface.connect_to_server()
print("Starting Training - id={}; num_opponents={}; num_teammates={}; "
"num_episodes={};".format(hfo_interface.hfo.getUnum(), num_op,
num_team, num_episodes))
# Agent set-up
reward_function = basic_reward
features_manager = DiscreteFeatures1TeammateV1(num_team, num_op)
actions_manager = DiscreteActions1TeammateV1()
# Q Agent:
agent = QLearningAgent(num_states=features_manager.get_num_states(),
num_actions=actions_manager.get_num_actions(),
learning_rate=0.1,
discount_factor=0.9,
