import numpy as np
from gym import spaces
from stable_baselines.common.vec_env import VecEnvWrapper
[docs]class VecFrameStack(VecEnvWrapper):
"""
Frame stacking wrapper for vectorized environment
:param venv: (VecEnv) the vectorized environment to wrap
:param n_stack: (int) Number of frames to stack
"""
def __init__(self, venv, n_stack):
self.venv = venv
self.n_stack = n_stack
wrapped_obs_space = venv.observation_space
low = np.repeat(wrapped_obs_space.low, self.n_stack, axis=-1)
high = np.repeat(wrapped_obs_space.high, self.n_stack, axis=-1)
self.stackedobs = np.zeros((venv.num_envs,) + low.shape, low.dtype)
observation_space = spaces.Box(low=low, high=high, dtype=venv.observation_space.dtype)
VecEnvWrapper.__init__(self, venv, observation_space=observation_space)
[docs] def step_wait(self):
observations, rewards, dones, infos = self.venv.step_wait()
self.stackedobs = np.roll(self.stackedobs, shift=-observations.shape[-1], axis=-1)
for i, done in enumerate(dones):
if done:
self.stackedobs[i] = 0
self.stackedobs[..., -observations.shape[-1]:] = observations
return self.stackedobs, rewards, dones, infos
[docs] def reset(self):
"""
Reset all environments
"""
obs = self.venv.reset()
self.stackedobs[...] = 0
self.stackedobs[..., -obs.shape[-1]:] = obs
return self.stackedobs
[docs] def close(self):
self.venv.close()