envWrapper.py

import cv2
import gym
import collections
import numpy as np

# Code from book 'Deep Reinforcement Learning Hands On' and then Stable Baselines common atari wrappers.
# Presses Fire button if required for game to start
class FireResetEnv(gym.Wrapper):
    def __init__(self, env=None):
        super(FireResetEnv, self).__init__(env)
        assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
        assert len(env.unwrapped.get_action_meanings()) >= 3

    def step(self, action):
        return self.env.step(action)

    def reset(self):
        self.env.reset()
        obs, _, done, _ = self.env.step(1)
        if done:
            self.env.reset()
        obs, _, done, _ = self.env.step(2)
        if done:
            self.env.reset()
        return obs


# Skip over 4 frames and then take max over for flash issues.
class MaxAndSkipEnv(gym.Wrapper):
    def __init__(self, env=None, skip=4):
        super(MaxAndSkipEnv, self).__init__(env)
        # most recent raw observations (for max pooling across time steps)
        self._obs_buffer = collections.deque(maxlen=2)
        self._skip = skip

    def step(self, action):
        total_reward = 0.0
        done = None
        for _ in range(self._skip):
            obs, reward, done, info = self.env.step(action)
            self._obs_buffer.append(obs)
            total_reward += reward
            if done:
                break
        max_frame = np.max(np.stack(self._obs_buffer), axis=0)
        return max_frame, total_reward, done, info

    def reset(self):
        self._obs_buffer.clear()
        obs = self.env.reset()
        self._obs_buffer.append(obs)
        return obs


# Reduce size and grayscale
class ProcessFrame84(gym.ObservationWrapper):
    def __init__(self, env=None):
        super(ProcessFrame84, self).__init__(env)
        self.observation_space = gym.spaces.Box(low=0, high=255, shape=(84, 84, 1), dtype=np.uint8)

    def observation(self, obs):
        return ProcessFrame84.process(obs)

    @staticmethod
    def process(frame):
        # print(frame.size)
        if frame.size == 210 * 160 * 3:
            img = np.reshape(frame, [210, 160, 3]).astype(np.float32)
        elif frame.size == 250 * 160 * 3:
            img = np.reshape(frame, [250, 160, 3]).astype(np.float32)
        else:
            assert False, "Unknown resolution."
        # Gray scale
        img = img[:, :, 0] * 0.299 + img[:, :, 1] * 0.587 + img[:, :, 2] * 0.114
        # CV2 resize takes in img and desired size. INTER AREA is resampling using pixel area relation. Can try INTER NEAREST.
        resized_screen = cv2.resize(img, (84, 110), interpolation=cv2.INTER_AREA)
        # Then reshape.
        x_t = resized_screen[18:102, :]
        x_t = np.reshape(x_t, [84, 84, 1])
        return x_t.astype(np.uint8)


# Stack frames to show action, four is recommended.
class BufferWrapper(gym.ObservationWrapper):
    def __init__(self, env, n_steps=4, dtype=np.float32):
        super(BufferWrapper, self).__init__(env)
        self.dtype = dtype
        old_space = env.observation_space
        self.observation_space = gym.spaces.Box(old_space.low.repeat(n_steps, axis=0),
                                                old_space.high.repeat(n_steps, axis=0), dtype=dtype)

    def reset(self):
        self.buffer = np.zeros_like(self.observation_space.low, dtype=self.dtype)
        return self.observation(self.env.reset())

    def observation(self, observation):
        self.buffer[:-1] = self.buffer[1:]
        self.buffer[-1] = observation
        return self.buffer


# BATCH CHANNEL HEIGHT WITH TO BATCH WIDTH HEIGHT CHANNEL
# convert to BCWH from BWHC
class ImageToPyTorch(gym.ObservationWrapper):
    def __init__(self, env):
        super(ImageToPyTorch, self).__init__(env)
        old_shape = self.observation_space.shape
        # Show that shape W and C are swapped
        self.observation_space = gym.spaces.Box(low=0.0, high=1.0, shape=(old_shape[-1], 
                                old_shape[0], old_shape[1]), dtype=np.float32)

    def observation(self, observation):
        return np.moveaxis(observation, 2, 0)


# Apparently can get better results sometimes if 0 to 255
# Normalize the numbers to make it easier to work with. 
class ScaledFloatFrame(gym.ObservationWrapper):
    def observation(self, obs):
        return np.array(obs).astype(np.float32) / 255.0

def makeEnv(ENV_NAME):
    env = gym.make(ENV_NAME)
    env = MaxAndSkipEnv(env)
    env = FireResetEnv(env)
    env = ProcessFrame84(env)
    env = ImageToPyTorch(env)
    env = BufferWrapper(env, 4)
    env = ScaledFloatFrame(env)
    return env