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 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 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 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 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 move_agent(self, action_name, game_interface: HFOAttackingPlayer,
features: DiscFeatures1Teammate):
""" Agent Moves/Dribbles in a specific direction """
# 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 game_interface.get_game_status(), \
game_interface.get_observation_array()
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))
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
start_pos_list = list(ORIGIN_POSITIONS.keys())
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())
# Set game start condition:
if not start_pos_list:
start_pos_list = list(ORIGIN_POSITIONS.keys())
start_pos = start_pos_list.pop()
go_to_origin_position(game_interface=game_interface,
pos_name=start_pos,
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 game_interface.get_game_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
