class PlaygroundEnv(gym.Env):
"""Custom Environment that follows gym interface"""
metadata = {'render.modes': ['human', 'rgb_array']}
def __init__(self, config):
playground_name = config['playground_name']
agent_type = config['agent_type']
sensors_name = config['sensors_name']
seed = config.get('seed', 0)
continuous_action_space = config.get('continuous_action_space', True)
multisteps = config.get('multisteps')
seed = (seed + id(self)) % (2**32)
random.seed(seed)
np.random.seed(seed)
self.video_dir = config.get('video_dir')
self.playground = PlaygroundRegister.playgrounds[playground_name[0]][
playground_name[1]]()
self.playground.time_limit = 1000
self.time_limit = self.playground.time_limit
self.episodes = 0
self._create_agent(agent_type, sensors_name)
self._set_action_space(continuous_action_space)
self._set_obs_space()
self.multisteps = None
if multisteps is not None:
assert isinstance(multisteps, int)
self.multisteps = multisteps
def _set_obs_space(self):
d = {}
for sensor in self.agent.sensors:
if isinstance(sensor.shape, int):
shape = (sensor.shape, 1)
else:
shape = sensor.shape
d[sensor.name] = spaces.Box(low=0,
high=1,
shape=shape,
dtype=np.float32)
self.observation_space = spaces.Dict(d)
def _set_action_space(self, continuous_action_space):
actuators = self.agent.controller.controlled_actuators
self.continuous_action_space = continuous_action_space
if self.continuous_action_space:
lows = []
highs = []
for actuator in actuators:
lows.append(actuator.min)
highs.append(actuator.max)
self.action_space = spaces.Box(
low=np.array(lows).astype(np.float32),
high=np.array(highs).astype(np.float32))
else:
# TODO:
raise NotImplementedError()
dims = []
for actuator in actuators:
dims.append(2)
self.action_space = spaces.MultiDiscrete(dims)
def _create_agent(self, agent_type, sensors_name):
if agent_type == 'base':
agent_cls = agents.BaseAgent
elif agent_type == 'arm':
agent_cls = agents.FullAgent
else:
raise ValueError(f"Wrong agent_type: {agent_type}")
agent = agent_cls(controller=controllers.External())
for sensor_name, sensor_params in get_sensor_params(sensors_name):
if sensor_name == 'depth':
sensor_cls = sensors.Lidar
sensor_name = 'depth_0'
elif sensor_name == 'rgb':
sensor_cls = sensors.RgbCamera
sensor_name = 'rgb_0'
elif sensor_name == 'touch':
sensor_cls = sensors.Touch
sensor_name = 'touch_0'
elif sensor_name == 'blind':
sensor_cls = sensors.BlindCamera
sensor_name = 'blind_0'
else:
raise NotImplementedError(
f'Sensor {sensor_name} not implemented')
agent.add_sensor(
sensor_cls(anchor=agent.base_platform,
normalize=True,
invisible_elements=agent.parts,
name=sensor_name,
**sensor_params))
self.playground.add_agent(agent)
self.game = Engine(self.playground, screen=False)
self.agent = agent
assert self.agent in self.game.agents
@property
def engine(self):
return self.game
def get_current_timestep(self):
return self.game.elapsed_time
def step(self, actions):
actions_to_game_engine = {}
actions_dict = {}
actuators = self.agent.controller.controlled_actuators
for actuator, action in zip(actuators, actions):
actuator.apply_action(action)
actions_to_game_engine[self.agent] = actions_dict
# Generate actions for other agents
for agent in self.game.agents:
if agent is not self.agent:
actions_to_game_engine[agent] = \
agent.controller.generate_actions()
if self.multisteps is None:
self.game.step(actions_to_game_engine)
else:
self.game.multiple_steps(actions_to_game_engine,
n_steps=self.multisteps)
self.game.update_observations()
reward = self.agent.reward
done = self.playground.done or not self.game.game_on
return (self.observations, reward, done, {})
@property
def observations(self):
sensor_values = {}
for sensor in self.agent.sensors:
sensor_values[sensor.name] = sensor.sensor_values
return sensor_values
def reset(self):
self.game.reset()
self.game.elapsed_time = 0
self.episodes += 1
self.game.update_observations()
return self.observations
def render(self, mode='human'):
if self.video_dir is None:
return None
img = self.game.generate_agent_image(self.agent)
img = (255 * img).astype(np.uint8)
step_id = self.game.elapsed_time
video_dir = osp.join(self.video_dir, str(id(self)), str(self.episodes))
frame_path = osp.join(video_dir, f"f-{step_id:03d}.png")
if not osp.exists(video_dir):
os.makedirs(video_dir, exist_ok=True)
cv2.imwrite(frame_path, img)
return img
def close(self):
self.game.terminate()
for part in agent.parts:
available_actions = part.get_available_actions()
for action in available_actions:
all_actions[agent.name][part.name][action.action.name] = [
actions[agent.name][part.name][action.action]
]
elif engine.total_elapsed_time > 0 and SAVE_SIMU:
for part in agent.parts:
available_actions = part.get_available_actions()
for action in available_actions:
all_actions[agent.name][part.name][
action.action.name].append(
actions[agent.name][part.name][action.action])
if engine.total_elapsed_time == 0: # Pour contourner un petit bug temporaire
engine.step(actions)
else:
engine.multiple_steps(actions, n_steps=2)
if SAVE_SIMU or SHOW_ENVIRONMENT:
engine.update_observations()
observation = IR_sensor.sensor_value
if SAVE_SIMU:
for agent in engine.agents:
for sensor in agent.sensors:
observation = sensor.sensor_value
sensor_name = sensor.name
if engine.total_elapsed_time == 1:
all_observations[agent.name][sensor_name] = [observation]
else:
class PlaygroundEnv(gym.Env):
"""Custom Environment that follows gym interface"""
metadata = {'render.modes': ['human']}
def __init__(self, config):
super().__init__()
playground_name = config['playground_name']
agent_type = config['agent_type']
sensors_name = config['sensors_name']
seed = config.get('seed', 0)
continuous_action_space = config.get('continuous_action_space', True)
multisteps = config.get('multisteps')
controller = config.get('controller', controllers.External())
self.playground = PlaygroundRegister.playgrounds[playground_name[0]][
playground_name[1]]()
seed = (seed + id(self)) % (2**32)
random.seed(seed)
np.random.seed(seed)
if agent_type == 'base':
agent_cls = agents.BaseAgent
elif agent_type == 'arm':
agent_cls = agents.FullAgent
else:
raise ValueError(f"Wrong agent_type: {agent_type}")
agent = agent_cls(platform=ForwardBackwardPlatform,
controller=controller)
for sensor_name, sensor_params in get_sensor_params(sensors_name):
if sensor_name == 'depth':
agent.add_sensor(
sensors.Lidar(anchor=agent.base_platform,
normalize=True,
**sensor_params))
elif sensor_name == 'rgb':
agent.add_sensor(
sensors.RgbCamera(anchor=agent.base_platform,
normalize=True,
**sensor_params))
elif sensor_name == 'touch':
agent.add_sensor(
sensors.Touch(anchor=agent.base_platform,
normalize=True,
**sensor_params))
self.playground.add_agent(agent)
self.time_limit = self.playground.time_limit
self.game = Engine(self.playground, screen=False)
self.agent = agent
assert self.agent in self.game.agents
# Define action space
actuators = self.agent.get_all_actuators()
self.continuous_action_space = continuous_action_space
self.actions_dict = {}
if self.continuous_action_space:
lows = []
highs = []
for actuator in actuators:
if actuator.action_range is ActionTypes.DISCRETE:
lows.append(-1)
highs.append(1)
elif actuator.action_range is ActionTypes.CONTINUOUS_CENTERED:
lows.append(actuator.min)
highs.append(actuator.max)
elif actuator.action_range is ActionTypes.CONTINUOUS_NOT_CENTERED:
lows.append(actuator.min)
highs.append(actuator.max)
else:
raise ValueError(f"Action type {actuator.action} unknown")
# lows.append(actuator.min)
# highs.append(actuator.max)
self.action_space = spaces.Box(
low=np.array(lows).astype(np.float32),
high=np.array(highs).astype(np.float32))
else:
dims = []
for actuator in actuators:
if actuator.action_range is ActionTypes.DISCRETE:
dims.append(2)
elif actuator.action_range is ActionTypes.CONTINUOUS_NOT_CENTERED:
dims.append(2)
else:
raise ValueError(f"Action type {actuator.action} unknown")
self.action_space = spaces.MultiDiscrete(dims)
# Define observation space
# Normalize all sensors to make sure they are in the same range
height_all_sensors, width_all_sensors, depth_all_sensors = 1, 0, 0
for sensor in self.agent.sensors:
if sensor.sensor_modality is SensorTypes.SEMANTIC:
raise ValueError('Semantic sensors not supported')
sensor.normalize = True
if isinstance(sensor.shape, int):
width_all_sensors = max(width_all_sensors, sensor.shape)
depth_all_sensors += 1
elif len(sensor.shape) == 2:
width_all_sensors = max(width_all_sensors, sensor.shape[0])
depth_all_sensors += sensor.shape[1]
else:
raise NotImplementedError
# width_all_sensors = max(width_all_sensors, sensor.shape[0])
# height_all_sensors += sensor.shape[1]
self.observation_space = spaces.Box(low=0,
high=1,
shape=(1, width_all_sensors,
depth_all_sensors),
dtype=np.float32)
self.observations = np.zeros(
(height_all_sensors, width_all_sensors, depth_all_sensors))
# Multisteps
self.multisteps = None
if multisteps is not None:
assert isinstance(multisteps, int)
self.multisteps = multisteps
@property
def engine(self):
return self.game
def get_current_timestep(self):
return self.game.elapsed_time
def step(self, actions):
# First, send actions to game engint
actions_to_game_engine = {}
actions_dict = {}
# Convert Stable-baselines actions into game engine actions
for actuator, action in zip(self.agent.get_all_actuators(), actions):
action_type = actuator.action
converted_action = action
# convert discrete action to binry
if self.continuous_action_space and action_type is ActionTypes.DISCRETE:
converted_action = 0 if converted_action < 0 else 1
# convert continuous actions in [-1, 1]
elif (not self.continuous_action_space) and (
action_type is ActionTypes.CONTINUOUS_CENTERED):
converted_action = converted_action - 1
actions_dict[actuator] = converted_action
actions_to_game_engine[self.agent] = actions_dict
# Generate actions for other agents
for agent in self.game.agents:
if agent is not self.agent:
actions_to_game_engine[
agent] = agent.controller.generate_actions()
# Now that we have all ctions, run the engine, and get the observations
if self.multisteps is None:
terminate = self.game.step(actions_to_game_engine)
else:
terminate = self.game.multiple_steps(actions_to_game_engine,
n_steps=self.multisteps)
self.game.update_observations()
# Concatenate the observations in a format that stable-baselines understands
sensor_values = []
for sensor in self.agent.sensors:
if isinstance(sensor.shape, int):
sensor_values.append(sensor.sensor_values[np.newaxis, :,
np.newaxis])
# self.observations[0, :sensor.shape,
# current_channel] = sensor.sensor_values[:]
# current_channel += 1
elif len(sensor.shape) == 2:
sensor_values.append(sensor.sensor_values[np.newaxis, :])
# self.observations[0, :sensor.shape[0],
# current_channel] = sensor.sensor_values[:, :]
# current_channel += sensor.shape[1]
else:
raise NotImplementedError
# self.observations[:sensor.shape[0], :sensor.
# shape[1], :] = sensor.sensor_values[:, :, :]
# current_channel += sensor.shape[2]
self.observations = np.concatenate(sensor_values, axis=2)
reward = self.agent.reward
done = self.playground.done or terminate
return (self.observations, reward, done, {})
def reset(self):
self.game.reset()
self.game.elapsed_time = 0
return np.zeros(self.observations.shape)
def render(self, mode='human'):
img = self.game.generate_playground_image()
return img
def close(self):
self.game.terminate()