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 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 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 train(num_train_episodes: int, num_total_train_ep: int,
game_interface: HFOAttackingPlayer, features: DiscreteFeatures,
agent: QLearningAgentTest, actions: DiscreteActionsV5, 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
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
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
status, observation = execute_action(
action_params=hfo_action_params,
repetitions=num_rep,
has_ball=has_ball,
game_interface=game_interface)
features.update_features(observation)
reward = reward_funct(status)
else:
# Act:
action_idx = agent.act(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 environment features:
reward = reward_funct(status)
sum_score += reward
features.update_features(observation)
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())
agent.learn_buffer(reward)
agent.update_hyper_parameters(num_total_episodes=num_total_train_ep)
# Game Reset
game_interface.reset()
print("## AVR Train reward = ", sum_score / num_train_episodes)
num_episodes = (num_train_ep + num_test_ep) * num_repetitions
# Directory
save_dir = args.save_dir or mkdir()
print("Starting Training - id={}; num_opponents={}; num_teammates={}; "
"num_episodes={};".format(agent_id, num_op, num_team, num_episodes))
# 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()
# Agent set-up
reward_function = basic_reward
features_manager = DiscreteFeatures(num_team, num_op)
actions_manager = DiscreteActionsV5()
agent = QLearningAgentTest(num_states=features_manager.get_num_states(),
num_actions=actions_manager.get_num_actions(),
learning_rate=0.1,
discount_factor=0.9,
epsilon=0.8)
# Save metrics structures
avr_rewards_list = []
avr_epsilons_list = []
trained_eps_list = []
q_tables_list = []
# Test one first time without previous train:
av_reward = test(num_episodes=num_test_ep,
agent=agent,
game_interface=hfo_interface,
features=features_manager,
save_dir = args.save_dir
print("Q Table file: ", load_file)
print("Starting Test - num_opponents={}; num_teammates={}; "
"num_episodes={};".format(num_op, num_team, num_episodes))
# 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()
# Agent set-up
reward_function = reward_functions.basic_reward
features_manager = discrete_features_v2.DiscreteFeaturesV2(
num_team, num_op)
actions_manager = DiscreteActionsV5(
origin_pos=learning_agent.ORIGIN_POSITIONS["MID LEFT"])
agent = learning_agent.QLearningAgentV5(
num_states=features_manager.get_num_states(),
num_actions=actions_manager.get_num_actions(),
dir=save_dir)
agent.load_q_table(load_file)
# Run training using Q-Learning
av_reward = learning_agent.test(num_episodes=num_episodes,
agent=agent,
game_interface=hfo_interface,
features=features_manager,
actions=actions_manager,
reward_funct=reward_function)
print("Av reward = {}".format(av_reward))
def train(num_train_episodes: int, num_total_train_ep: int,
game_interface: HFOAttackingPlayer,
features: discrete_features_v2.DiscreteFeaturesV2,
agent: QLearningAgentV5, actions: DiscreteActionsV5,
save_metrics: bool, 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 save_metrics: flag, if true save the metrics;
@param reward_funct: reward function used
@return: (QLearningAgentV5) the agent
"""
for ep in range(num_train_episodes):
# 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
aux_positions_names = set()
aux_actions_played = set()
while game_interface.in_game():
# Update environment features:
curr_state_id = features.get_state_index()
has_ball = features.has_ball()
# Act:
action_idx = agent.act(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:
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)
# Save metrics:
if save_metrics:
agent.save_visited_state(curr_state_id, action_idx)
agent.cum_reward += reward
aux_positions_names.add(features.get_position_name())
action_name = actions.map_action_to_str(action_idx, has_ball)
aux_actions_played.add(action_name)
# Update environment features:
prev_state_id = curr_state_id
features.update_features(observation)
curr_state_id = features.get_state_index()
agent.store_ep(state_idx=prev_state_id,
action_idx=action_idx,
reward=reward,
next_state_idx=curr_state_id,
has_ball=has_ball,
done=not game_interface.in_game())
agent.learn()
# print(':: Episode: {}; reward: {}; epsilon: {}; positions: {}; '
# 'actions: {}'.format(ep, agent.cum_reward, agent.epsilon,
# aux_positions_names, aux_actions_played))
if save_metrics:
agent.save_metrics(agent.old_q_table, agent.q_table)
# Reset player:
agent.reset()
agent.update_hyper_parameters(episode=agent.train_eps,
num_total_episodes=num_total_train_ep)
# Game Reset
game_interface.reset()
agent.save_model()
if save_metrics:
actions_name = [
actions_manager.map_action_to_str(i, has_ball=True)
for i in range(agent.num_actions)
]
agent.export_metrics(training=True, actions_name=actions_name)
return agent
def train(num_train_episodes: int, num_total_train_ep: int,
game_interface: HFOAttackingPlayer, features: DiscreteFeaturesV2,
agent: QLearningAgentV6, actions: DiscreteActionsV5, 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)
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 environment features:
reward = reward_funct(status)
sum_score += reward
features.update_features(observation)
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 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))