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_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))
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,
epsilon=0.6,
final_epsilon=0.1)
agent.load_q_table(model_file)
# save original q_table
save_model(q_table=agent.q_table,
file_name="original_model",
directory=save_dir)
# Test one first time without previous train:
av_reward = test(num_episodes=num_test_ep,
agent=agent,
game_interface=hfo_interface,
features=features_manager,
actions=actions_manager,
reward_funct=reward_function)
# 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()
agent = QLearningAgent(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)
# Test one first time without previous train:
av_reward = test(num_episodes=num_test_ep,
agent=agent,
game_interface=hfo_interface,
features=features_manager,
actions=actions_manager,
reward_funct=reward_function)
# Save metrics structures
trained_eps_list = [0]
avr_epsilons_list = [agent.epsilon]
avr_rewards_list = [av_reward]
qlearning_variation_list = [0]
args = parser.parse_args()
num_team = args.num_teammates
num_op = args.num_opponents
num_episodes = args.num_ep
load_file = args.load_file
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(num_opponents=num_op,
num_teammates=num_team)
hfo_interface.connect_to_server()
# Agent set-up
reward_function = basic_reward
features_manager = DiscreteFeatures1TeammateV1(num_team, num_op)
actions_manager = DiscreteActions1TeammateV1()
agent = QLearningAgent(num_states=features_manager.get_num_states(),
num_actions=actions_manager.get_num_actions())
agent.load_q_table(load_file)
# Run training using Q-Learning
av_reward = 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))