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"""
Author: Minh Pham-Dinh
Created: Feb 4th, 2024
Last Modified: Feb 7th, 2024
Email: mhpham26@colby.edu
Description:
File containing wrappers for different environment types.
"""
import gymnasium as gym
from dm_control import suite
from dm_control.suite.wrappers import pixels
import numpy as np
import cv2
import os
from dm_control import suite
from dm_control.rl.control import Environment
#wrapper by Hafner et al
class ActionRepeat:
def __init__(self, env, repeats):
self.env = env
self.repeats = repeats
def __getattr__(self, name):
return getattr(self.env, name)
def step(self, action):
done = False
total_reward = 0
current_step = 0
while current_step < self.repeats and not done:
obs, reward, termination, truncation, info = self.env.step(action)
total_reward += reward
current_step += 1
done = termination or truncation
return obs, total_reward, termination, truncation, info
#wrapper by Hafner et al
class NormalizeActions:
"""
A wrapper class that normalizes the action space of an environment.
Args:
env (gym.Env): The environment to be wrapped.
Attributes:
_env (gym.Env): The original environment.
_mask (numpy.ndarray): A boolean mask indicating which action dimensions are finite.
_low (numpy.ndarray): The lower bounds of the action space.
_high (numpy.ndarray): The upper bounds of the action space.
"""
def __init__(self, env):
self._env = env
self._mask = np.logical_and(
np.isfinite(env.action_space.low),
np.isfinite(env.action_space.high))
self._low = np.where(self._mask, env.action_space.low, -1)
self._high = np.where(self._mask, env.action_space.high, 1)
def __getattr__(self, name):
"""
Delegate attribute access to the original environment.
Args:
name (str): The name of the attribute.
Returns:
Any: The value of the attribute in the original environment.
"""
return getattr(self._env, name)
@property
def action_space(self):
"""
Get the normalized action space.
Returns:
gym.spaces.Box: The normalized action space.
"""
low = np.where(self._mask, -np.ones_like(self._low), self._low)
high = np.where(self._mask, np.ones_like(self._low), self._high)
return gym.spaces.Box(low, high, dtype=np.float32)
def step(self, action):
"""
Take a step in the environment with a normalized action.
Args:
action (numpy.ndarray): The normalized action.
Returns:
Tuple: A tuple containing the next state, reward, done flag, and additional information.
"""
original = (action + 1) / 2 * (self._high - self._low) + self._low
original = np.where(self._mask, original, action)
return self._env.step(original)
class DMCtoGymWrapper(gym.Env):
"""
Wrapper to convert a DeepMind Control Suite environment to a Gymnasium environment with additional features like recording and episode truncation.
Args:
domain_name (str): The name of the domain.
task_name (str): The name of the task.
task_kwargs (dict, optional): Additional kwargs for the task.
visualize_reward (bool, optional): Whether to visualize the reward. Defaults to False.
resize (list, optional): New size to resize observations. Defaults to [64, 64].
record (bool, optional): Whether to record episodes. Defaults to False.
record_freq (int, optional): Frequency (in episodes) to record. Defaults to 100.
record_path (str, optional): Path to save recorded videos. Defaults to '../'.
max_episode_steps (int, optional): Maximum steps per episode for truncation. Defaults to 1000.
"""
def __init__(self, domain_name, task_name, task_kwargs=None, visualize_reward=False, resize=[64,64], record=False, record_freq=100, record_path='../', max_episode_steps=1000, camera=None):
super().__init__()
self.env = suite.load(domain_name, task_name, task_kwargs=task_kwargs, visualize_reward=visualize_reward)
self.episode_count = -1
self.record = record
self.record_freq = record_freq
self.record_path = record_path
self.max_episode_steps = max_episode_steps
self.current_step = 0
self.total_reward = 0
self.recorder = None
# Define action and observation space based on the DMC environment
action_spec = self.env.action_spec()
self.action_space = gym.spaces.Box(low=action_spec.minimum, high=action_spec.maximum, dtype=np.float32)
# Initialize the pixels wrapper for observation space
self.env = pixels.Wrapper(self.env, pixels_only=True)
self.resize = resize # Assuming RGB images
self.observation_space = gym.spaces.Box(low=-0.5, high=+0.5, shape=(3, *resize), dtype=np.float32)
if camera is None:
camera = dict(quadruped=2).get(domain_name, 0)
self._camera = camera
def step(self, action):
time_step = self.env.step(action)
obs = self._get_obs(self.env)
reward = time_step.reward if time_step.reward is not None else 0
self.total_reward += (reward or 0)
self.current_step += 1
termination = time_step.last()
truncation = (self.current_step == self.max_episode_steps)
info = {}
if termination or truncation:
info = {
'episode': {
'r': [self.total_reward],
'l': self.current_step
}
}
if self.recorder:
frame = cv2.cvtColor(self.env.physics.render(camera_id=self._camera), cv2.COLOR_RGB2BGR)
self.recorder.write(frame)
video_file = os.path.join(self.record_path, f"episode_{self.episode_count}.webm")
if termination or truncation:
self._reset_recorder()
info['video_path'] = video_file
return obs, reward, termination, truncation, info
def reset(self):
self.current_step = 0
self.total_reward = 0
self.episode_count += 1
time_step = self.env.reset()
obs = self._get_obs(self.env)
if self.record and self.episode_count % self.record_freq == 0:
self._start_recording(self.env.physics.render(camera_id=self._camera))
return obs, {}
def _start_recording(self, frame):
if not os.path.exists(self.record_path):
os.makedirs(self.record_path)
video_file = os.path.join(self.record_path, f"episode_{self.episode_count}.webm")
height, width, _ = frame.shape
self.recorder = cv2.VideoWriter(video_file, cv2.VideoWriter_fourcc(*'vp80'), 30, (width, height))
self.recorder.write(frame)
def _reset_recorder(self):
if self.recorder:
self.recorder.release()
self.recorder = None
def _get_obs(self, env):
obs = self.render()
obs = obs/255 - 0.5
rearranged_obs = obs.transpose([2,0,1])
return rearranged_obs
def render(self, mode='rgb_array'):
return self.env.physics.render(*self.resize, camera_id=self._camera) # Adjust camera_id based on the environment
class AtariPreprocess(gym.Wrapper):
"""
A custom Gym wrapper that integrates multiple environment processing steps:
- Records episode statistics and videos.
- Resizes observations to a specified shape.
- Scales and reorders observation channels.
- Scales rewards using the tanh function.
Parameters:
- env (gym.Env): The original environment to wrap.
- new_obs_size (tuple): The target size for observation resizing (height, width).
- record (bool): If True, enable video recording.
- record_path (str): The directory path where videos will be saved.
- record_freq (int): Frequency (in episodes) at which to record videos.
"""
def __init__(self, env, new_obs_size, record=False, record_path='../videos/', record_freq=100):
super().__init__(env)
self.env = gym.wrappers.RecordEpisodeStatistics(env)
if record:
self.env = gym.wrappers.RecordVideo(self.env, record_path, episode_trigger=lambda episode_id: episode_id % record_freq == 0)
self.env = gym.wrappers.ResizeObservation(self.env, shape=new_obs_size)
self.new_obs_size = new_obs_size
self.observation_space = gym.spaces.Box(
low=-0.5, high=0.5,
shape=(3, new_obs_size[0], new_obs_size[1]),
dtype=np.float32
)
def step(self, action):
obs, reward, termination, truncation, info = super().step(action)
obs = self.process_observation(obs)
reward = np.tanh(reward) # Scale reward
return obs, reward, termination, truncation, info
def reset(self, **kwargs):
obs, info = super().reset(**kwargs)
obs = self.process_observation(obs)
return obs, info
def process_observation(self, observation):
"""
Process and return the observation from the environment.
- Scales pixel values to the range [-0.5, 0.5].
- Reorders channels to CHW format (channels, height, width).
Parameters:
- observation (np.ndarray): The original observation from the environment.
Returns:
- np.ndarray: The processed observation.
"""
if 'pixels' in observation:
observation = observation['pixels']
observation = observation / 255.0 - 0.5
observation = np.transpose(observation, (2, 0, 1))
return observation |