class DdpgWrapper(gym.Wrapper):
def __init__(self, env, args):
gym.Wrapper.__init__(self, env)
self.state_transform = StateVelCentr(
obstacles_mode='standard',
exclude_centr=True,
vel_states=[])
self.observation_space = Box(-1000, 1000, self.state_transform.state_size)
self.skip_frames = args.skip_frames
self.reward_scale = args.reward_scale
self.fail_reward = args.fail_reward
# [-1, 1] <-> [0, 1]
action_mean = .5
action_std = .5
self.normalize_action = lambda x: (x - action_mean) / action_std
self.denormalise_action = lambda x: x * action_std + action_mean
def reset(self, **kwargs):
return self._reset(**kwargs)
def _reset(self, **kwargs):
observation = self.env.reset(**kwargs)
self.env_step = 0
self.state_transform.reset()
observation, _ = self.state_transform.process(observation)
observation = self.observation(observation)
return observation
def _step(self, action):
action = self.denormalise_action(action)
total_reward = 0.
for _ in range(self.skip_frames):
observation, reward, done, _ = self.env.step(action)
observation, obst_rew = self.state_transform.process(observation)
total_reward += reward + obst_rew
self.env_step += 1
if done:
if self.env_step < 1000: # hardcoded
total_reward += self.fail_reward
break
observation = self.observation(observation)
total_reward *= self.reward_scale
return observation, total_reward, done, None
def observation(self, observation):
return self._observation(observation)
def _observation(self, observation):
observation = np.array(observation, dtype=np.float32)
return observation
def __init__(self, env, args):
gym.Wrapper.__init__(self, env)
self.state_transform = StateVelCentr(
obstacles_mode='standard',
exclude_centr=True,
vel_states=[])
self.observation_space = Box(-1000, 1000, self.state_transform.state_size)
self.skip_frames = args.skip_frames
self.reward_scale = args.reward_scale
self.fail_reward = args.fail_reward
# [-1, 1] <-> [0, 1]
action_mean = .5
action_std = .5
self.normalize_action = lambda x: (x - action_mean) / action_std
self.denormalise_action = lambda x: x * action_std + action_mean
def create_observation_handler(args):
if hasattr(args, "baseline_wrapper") or hasattr(args, "ddpg_wrapper"):
state_transform = StateVelCentr(
obstacles_mode='standard',
exclude_centr=True,
vel_states=[])
def observation_handler(observation, previous_action=None):
observation = np.array(observation, dtype=np.float32)
observation, _ = state_transform.process(observation)
return observation
else:
def observation_handler(observation, previous_action=None):
observation = np.array(observation, dtype=np.float32)
return observation
return observation_handler