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envs.py
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envs.py
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import gym
import numpy as np
import torch
from gym.spaces.box import Box
from wrapper.benchmarks import *
from wrapper.monitor import *
from wrapper.vec_env import VecEnvWrapper
from wrapper.shmem_vec_env import ShmemVecEnv
from wrapper.dummy_vec_env import DummyVecEnv
try:
import dm_control2gym
except ImportError:
pass
try:
import roboschool
except ImportError:
pass
try:
import pybullet_envs
except ImportError:
pass
def make_env(env_id, seed, rank, log_dir, allow_early_resets, args):
def _thunk():
if env_id.startswith("dm"):
_, domain, task = env_id.split('.')
env = dm_control2gym.make(domain_name=domain, task_name=task)
else:
if args.envName == 'Physics-v0':
env = gym.make(args.envName,
args = args
)
else:
assert False
env.seed(seed + rank)
obs_shape = env.observation_space.shape
if str(env.__class__.__name__).find('TimeLimit') >= 0:
env = TimeLimitMask(env)
if log_dir is not None:
env = Monitor(
env,
os.path.join(log_dir, str(rank)),
allow_early_resets=allow_early_resets)
if len(env.observation_space.shape) == 3:
raise NotImplementedError(
"CNN models work only for atari,\n"
"please use a custom wrapper for a custom pixel input env.\n")
# If the input has shape (W,H,3), wrap for PyTorch convolutions
obs_shape = env.observation_space.shape
if len(obs_shape) == 3 and obs_shape[2] in [1, 3]:
env = TransposeImage(env, op=[2, 0, 1])
return env
return _thunk
def make_vec_envs(args,
log_dir,
allow_early_resets):
env_name = args.envName
seed = args.seed
num_processes = args.num_processes
device = args.device
envs = [
make_env(env_name, seed, i, log_dir, allow_early_resets, args)
for i in range(num_processes)
]
"""
If you don't specify observation_space, we'll have to create a dummy
environment to get it.
"""
if args.envName == 'Physics-v0':
env = gym.make(args.envName,
args = args
)
else:
assert False
spaces = [env.observation_space, env.action_space]
try:
envs = ShmemVecEnv(envs, spaces, context='fork')
except ValueError:
envs = ShmemVecEnv(envs, spaces, context='spawn')
envs = VecPyTorch(envs, device)
return envs, spaces, env.obs_len
class TimeLimitMask(gym.Wrapper):
def step(self, action):
obs, rew, done, info = self.env.step(action)
if done and self.env._max_episode_steps == self.env._elapsed_steps:
info['bad_transition'] = True
return obs, rew, done, info
def reset(self, **kwargs):
return self.env.reset(**kwargs)
class TransposeObs(gym.ObservationWrapper):
def __init__(self, env=None):
"""
Transpose observation space (base class)
"""
super(TransposeObs, self).__init__(env)
class TransposeImage(TransposeObs):
def __init__(self, env=None, op=[2, 0, 1]):
"""
Transpose observation space for images
"""
super(TransposeImage, self).__init__(env)
assert len(op) == 3, "Error: Operation, {str(op)}, must be dim3"
self.op = op
obs_shape = self.observation_space.shape
self.observation_space = Box(
self.observation_space.low[0, 0, 0],
self.observation_space.high[0, 0, 0], [
obs_shape[self.op[0]], obs_shape[self.op[1]],
obs_shape[self.op[2]]
],
dtype=self.observation_space.dtype)
def observation(self, ob):
return ob.transpose(self.op[0], self.op[1], self.op[2])
class VecPyTorch(VecEnvWrapper):
def __init__(self, venv, device):
"""Return only every `skip`-th frame"""
super(VecPyTorch, self).__init__(venv)
self.device = device
# TODO: Fix data types
def reset(self):
obs = self.venv.reset()
obs = torch.from_numpy(np.array(obs)).float().to(self.device)
return obs
def step_async(self, actions):
if isinstance(actions, torch.LongTensor):
# Squeeze the dimension for discrete actions
actions = actions.squeeze(1)
self.venv.step_async(actions)
def step_wait(self):
obs, reward, done, info = self.venv.step_wait()
obs = torch.from_numpy(np.array(obs)).float().to(self.device)
reward = torch.from_numpy(reward).unsqueeze(dim=1).float()
return obs, reward, done, info