def train(num_episodes: int, game_interface: HFOAttackingPlayer,
features: discrete_features_v2.DiscreteFeaturesV2,
agent: QLearningAgentV4, actions: DiscreteActionsV2, 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)
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)
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())
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))
agent.save_metrics(agent.old_q_table, agent.q_table)
# Reset player:
agent.reset()
agent.update_hyper_parameters(episode=ep,
num_total_episodes=num_episodes)
# Game Reset
game_interface.reset()
agent.save_model()
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)
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