class GameManager:
def __init__(self, id):
self.visualize = False
if Config.VISUALIZE and int(id / len(Config.PATH_TO_WORLD)) == 0:
self.visualize = True
elif Config.PLAY_MODE:
self.visualize = True
world_name = Config.PATH_TO_WORLD[id % len(Config.PATH_TO_WORLD)]
self.env = Environment(world_name)
print("Env {} for Agent {} started.".format(world_name, id))
self.env.set_mode(Config.MODE, Config.TERMINATE_AT_END)
self.env.set_observation_rotation_size(Config.OBSERVATION_ROTATION_SIZE)
self.env.use_observation_rotation_size(Config.USE_OBSERVATION_ROTATION)
self.env.set_cluster_size(Config.CLUSTER_SIZE)
self.reset()
def reset(self):
observation, _, _, _ = self.env.reset()
input_laser, rotation = self.process_observation(observation)
map = StateMap(input_laser)
obs = np.array([ [map.S_image], [rotation] ])
return obs
def step(self, action):
self._update_display()
if action is None:
observation, reward, done, info = self.env.step(0, 0, 20)
input_laser, rotation = self.process_observation(observation)
map = StateMap(input_laser)
#obs = np.array([[map.States_map, map.Reward_map], [rotation]])
obs = np.array([[map.S_image], [rotation]])
reward = 0
done = False
else:
linear, angular = map_action(action)
observation, reward, done, info = self.env.step(linear, angular, 20)
input_laser, rotation = self.process_observation(observation)
map = StateMap(input_laser)
obs = np.array([[map.S_image], [rotation]])
return obs, reward, done, info
def _update_display(self):
if self.visualize:
self.env.visualize()
def observation_size(self):
return self.env.observation_size()
def process_observation(self, observation):
laser_scan = np.array(observation[:Config.OBSERVATION_SIZE])
oriontaion = np.array(observation[Config.OBSERVATION_SIZE:])
return laser_scan, oriontaion
class GameManager:
def __init__(self, id):
self.visualize = False
if Config.VISUALIZE and int(id / len(Config.PATH_TO_WORLD)) == 0:
self.visualize = True
elif Config.PLAY_MODE:
self.visualize = True
world_name = Config.PATH_TO_WORLD[id % len(Config.PATH_TO_WORLD)]
self.env = Environment(world_name)
print("Env {} for Agent {} started.".format(world_name, id))
self.env.set_mode(Config.MODE, Config.TERMINATE_AT_END)
self.env.set_observation_rotation_size(
Config.OBSERVATION_ROTATION_SIZE)
self.env.use_observation_rotation_size(Config.USE_OBSERVATION_ROTATION)
self.env.set_cluster_size(Config.CLUSTER_SIZE)
self.reset()
def reset(self):
observation, _, _, _ = self.env.reset()
return observation
def step(self, action):
self._update_display()
if action is None:
observation, reward, done, info = self.env.step(0, 0, 20)
reward = 0
done = False
else:
linear, angular = map_action(action)
observation, reward, done, info = self.env.step(
linear, angular, 20)
return observation, reward, done, info
def _update_display(self):
if self.visualize:
self.env.visualize()
def observation_size(self):
return self.env.observation_size()
def process_reward(self, reward):
return reward
def process_info(self, info):
return info
def process_action(self, action):
return action
def process_state_batch(self, batch):
return batch[:, 0, :]
env = Environment("Simulation2d/svg/proto_4", 4)
env.use_observation_rotation_size(True)
env.set_observation_rotation_size(128)
env.set_mode(Mode.ALL_RANDOM)
processor = DQNAgentProc()
states = env.observation_size()
actions = action_mapper.ACTION_SIZE
if DEBUG:
print('states: {0}'.format(states))
print('actions: {0}'.format(actions))
def build_callbacks(env_name):
weights_filename = 'new_results/'+ env_name +'{step}.h5f'
log_filename = 'new_log/{}.json'.format(env_name)
callbacks = [ModelIntervalCheckpoint(weights_filename, interval=10000)]
callbacks += [FileLogger(log_filename, interval=1000)]
return callbacks
