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 __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)
class Player:
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 get_reward(self, game_status: int) -> int:
if game_status == GOAL:
return 1000
elif game_status in [CAPTURED_BY_DEFENSE, OUT_OF_BOUNDS, OUT_OF_TIME]:
return -1000
else:
return -1
def set_starting_game_conditions(self,
game_interface: HFOAttackingPlayer,
features: PlasticFeatures,
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 test(self, num_episodes: int, start_with_ball: bool = True) -> float:
"""
@param num_episodes: number of episodes to run
@param start_with_ball: 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:
try:
self.game_interface.check_server_is_up()
except ServerDownError as e:
print("!!SERVER DOWN!! TEST {}/{}".format(ep, num_episodes))
avr_win_rate = round(_num_wins / (ep + 1), 2)
print("[TEST: Summary] WIN rate = {};".format(avr_win_rate))
return avr_win_rate
# 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)
print("\nNEW TEST [{}]".format(
starting_pos_list[ep % len(starting_pos_list)]))
# print("FEATURES: ", self.features.get_features())
# Start learning loop
status = IN_GAME
prev_action_idx = None
while self.game_interface.in_game():
if self.features.has_ball():
# 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:
print("ACTION:: {}".format(
self.actions.map_action_to_str(action_idx)))
prev_action_idx = action_idx
self.actions.execute_action(
action_idx=action_idx,
features=self.features,
game_interface=self.game_interface)
else:
if prev_action_idx != -1:
print("ACTION:: MOVE!!")
prev_action_idx = -1
status = self.actions.no_ball_action(
features=self.features,
game_interface=self.game_interface)
# Update auxiliar variables:
if self.game_interface.scored_goal() or status == GOAL:
print("[GOAL]")
_num_wins += 1
else:
print("[FAIL]")
# Game Reset
self.game_interface.reset()
avr_win_rate = round(_num_wins / num_episodes, 2)
print("[TEST: Summary] WIN rate = {};".format(avr_win_rate))
return avr_win_rate
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
"""
starting_pos_list = list(STARTING_POSITIONS.values())
# metrics variables:
_num_wins = 0
_sum_epsilons = 0
for ep in range(num_train_episodes):
# Check if server still running:
try:
self.game_interface.check_server_is_up()
except ServerDownError as e:
print("!!SERVER DOWN!! TRAIN {}/{}".format(
ep, num_train_episodes))
return
# 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_pos=starting_pos_list[ep % len(starting_pos_list)],
start_with_ball=start_with_ball)
# Start learning loop
status = IN_GAME
episode_buffer = list()
while self.game_interface.in_game():
# Has Ball:
if self.features.has_ball():
# 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()
# Store transition:
# (obs, action, reward, new obs, done?)
transition = np.array([
features_array, action_idx,
self.get_reward(status),
self.features.get_features(), done
])
episode_buffer.append(transition)
# Train:
self.agent.train(terminal_state=done)
# No ball:
else:
status = self.actions.no_ball_action(
features=self.features,
game_interface=self.game_interface)
if self.game_interface.scored_goal() or status == GOAL:
_num_wins += 1
reward = self.get_reward(GOAL)
else:
reward = self.get_reward(status)
# Add episodes:
self.agent.store_episode(episode_buffer, reward=reward)
# Update auxiliar variables:
_sum_epsilons += self.agent.epsilon
# Update Agent:
self.agent.restart(num_total_train_ep)
# Game Reset
self.game_interface.reset()
avr_epsilon = round(_sum_epsilons / num_train_episodes, 3)
print("[TRAIN: Summary] WIN rate = {}; AVR epsilon = {}".format(
_num_wins / num_train_episodes, avr_epsilon))
return avr_epsilon
def train_offline(self, game_buffer: np.ndarray):
for _ in range(5):
buffer = game_buffer.copy()
self.agent.train_from_batch(buffer)
print("MODEL TRAINED")
aux = [-1] * 6
features_base = np.array(aux)
for idx in range(6):
features_array = features_base.copy()
features_array[idx] = 0
print("[TEST] {}".format(features_array.tolist()))
action_idx = self.agent.exploit_actions(features_array,
verbose=True)
print("-> {}".format(
self.actions.map_action_to_str(action_idx)))
args = parser.parse_args()
port = args.port
num_games = args.num_games
NUM_TEAMMATES = 0
NUM_OPPONENTS = 1
# Game Interface:
game_interface = HFOAttackingPlayer(num_opponents=NUM_OPPONENTS,
num_teammates=NUM_TEAMMATES, port=port)
game_interface.connect_to_server()
# Features Interface:
features = DiscFeatures1Teammate(num_op=NUM_OPPONENTS,
num_team=NUM_TEAMMATES)
# Actions Interface:
actions = Actions()
for ep in range(num_games):
# Update features:
features.update_features(game_interface.get_state())
# Set up gaming conditions:
actions.dribble_to_pos((-0.5, -0.7), features, game_interface)
# Start learning loop
status = IN_GAME
prev_action_idx = None
while game_interface.in_game():
if features.has_ball():
actions.shoot_ball(game_interface, features)
else:
class Player:
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 get_reward(self, status: int) -> int:
return basic_reward(status)
def set_starting_game_conditions(self, game_interface: HFOAttackingPlayer,
features: DiscFeatures1Teammate,
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 test(self, num_episodes: int, start_with_ball: bool = True) -> float:
"""
@param num_episodes: number of episodes to run
@param start_with_ball: 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)
print("\nNEW TEST [{}]".format(
starting_pos_list[ep % len(starting_pos_list)]))
# Start learning loop
status = IN_GAME
prev_action_idx = None
while self.game_interface.in_game():
if self.features.has_ball():
# 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:
print("ACTION:: {}".format(
self.actions.map_action_to_str(action_idx)))
prev_action_idx = action_idx
self.actions.execute_action(
action_idx=action_idx,
features=self.features,
game_interface=self.game_interface)
else:
if prev_action_idx != -1:
print("ACTION:: MOVE!!")
prev_action_idx = -1
status = self.actions.no_ball_action(
features=self.features,
game_interface=self.game_interface)
# Update auxiliar variables:
if self.game_interface.scored_goal() or status == GOAL:
print("[GOAL]")
_num_wins += 1
else:
print("[FAIL]")
# 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 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
status = IN_GAME
episode_buffer = list()
while self.game_interface.in_game():
# Has Ball:
if self.features.has_ball():
# 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()
# Store transition:
# (obs, action, reward, new obs, done?)
transition = np.array(
[features_array, action_idx, self.get_reward(status),
self.features.get_features(), done])
episode_buffer.append(transition)
# No ball:
else:
status = self.actions.no_ball_action(
features=self.features,
game_interface=self.game_interface)
if self.game_interface.scored_goal() or status == GOAL:
_num_wins += 1
reward = self.get_reward(GOAL)
else:
reward = self.get_reward(status)
self.agent.store_episode(episode_buffer, reward=reward)
# Train:
self.agent.train(terminal_state=True)
# Update auxiliar variables:
_sum_epsilons += self.agent.epsilon
# 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))