File size: 7,748 Bytes
079c32c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 |
from typing import Any, Union, List
import copy
import numpy as np
import gym
import matplotlib.pyplot as plt
import einops
import imageio
from easydict import EasyDict
from ding.envs import BaseEnv, BaseEnvTimestep
from ding.envs.common.env_element import EnvElement, EnvElementInfo
from ding.envs.common.common_function import affine_transform
from ding.torch_utils import to_ndarray, to_list
from .d4rl_wrappers import wrap_d4rl
from ding.utils import ENV_REGISTRY
MAZE_BOUNDS = {
'maze2d-umaze-v1': (0, 5, 0, 5),
'maze2d-medium-v1': (0, 8, 0, 8),
'maze2d-large-v1': (0, 9, 0, 12)
}
def plot2img(fig, remove_margins=True):
# https://stackoverflow.com/a/35362787/2912349
# https://stackoverflow.com/a/54334430/2912349
from matplotlib.backends.backend_agg import FigureCanvasAgg
if remove_margins:
fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
canvas = FigureCanvasAgg(fig)
canvas.draw()
img_as_string, (width, height) = canvas.print_to_buffer()
return np.fromstring(img_as_string, dtype='uint8').reshape((height, width, 4))
def zipsafe(*args):
length = len(args[0])
assert all([len(a) == length for a in args])
return zip(*args)
def zipkw(*args, **kwargs):
nargs = len(args)
keys = kwargs.keys()
vals = [kwargs[k] for k in keys]
zipped = zipsafe(*args, *vals)
for items in zipped:
zipped_args = items[:nargs]
zipped_kwargs = {k: v for k, v in zipsafe(keys, items[nargs:])}
yield zipped_args, zipped_kwargs
@ENV_REGISTRY.register('d4rl')
class D4RLEnv(BaseEnv):
def __init__(self, cfg: dict) -> None:
self._cfg = cfg
self._use_act_scale = cfg.use_act_scale
self._init_flag = False
if 'maze' in self._cfg.env_id:
self.observations = []
self._extent = (0, 1, 1, 0)
def reset(self) -> np.ndarray:
if not self._init_flag:
self._env = self._make_env(only_info=False)
self._env.observation_space.dtype = np.float32 # To unify the format of envs in DI-engine
self._observation_space = self._env.observation_space
if 'maze' in self._cfg.env_id:
new_low = np.tile(self._observation_space.low, 2)
new_high = np.tile(self._observation_space.high, 2)
self._observation_space = gym.spaces.Box(low=new_low, high=new_high)
self._action_space = self._env.action_space
self._reward_space = gym.spaces.Box(
low=self._env.reward_range[0], high=self._env.reward_range[1], shape=(1, ), dtype=np.float32
)
self._init_flag = True
if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed:
np_seed = 100 * np.random.randint(1, 1000)
self._env.seed(self._seed + np_seed)
elif hasattr(self, '_seed'):
self._env.seed(self._seed)
if 'maze' in self._cfg.env_id:
target = self._env.get_target()
self.target_obs = np.array([*target, 0, 0])
obs = self._env.reset()
if 'maze' in self._cfg.env_id:
self.observations.append(obs)
obs = np.hstack((obs, self.target_obs))
obs = to_ndarray(obs).astype('float32')
self._eval_episode_return = 0.
return obs
def close(self) -> None:
if self._init_flag:
self._env.close()
self._init_flag = False
def seed(self, seed: int, dynamic_seed: bool = True) -> None:
self._seed = seed
self._dynamic_seed = dynamic_seed
np.random.seed(self._seed)
def step(self, action: Union[np.ndarray, list]) -> BaseEnvTimestep:
action = to_ndarray(action)
if self._use_act_scale:
action_range = {'min': self.action_space.low[0], 'max': self.action_space.high[0], 'dtype': np.float32}
action = affine_transform(action, min_val=action_range['min'], max_val=action_range['max'])
obs, rew, done, info = self._env.step(action)
self._eval_episode_return += rew
if 'maze' in self._cfg.env_id:
self.observations.append(obs)
obs = np.hstack([obs, self.target_obs])
obs = to_ndarray(obs).astype('float32')
rew = to_ndarray([rew]) # wrapped to be transfered to a array with shape (1,)
if done:
info['eval_episode_return'] = self._eval_episode_return
# self.composite('/mnt/PD/render/rollout.png',self.observations,ncol=1)
return BaseEnvTimestep(obs, rew, done, info)
def renders(self, observations, conditions=None, title=None):
bounds = MAZE_BOUNDS[self._cfg.env_id]
observations = observations + .5
if len(bounds) == 2:
_, scale = bounds
observations /= scale
elif len(bounds) == 4:
_, iscale, _, jscale = bounds
observations[:, 0] /= iscale
observations[:, 1] /= jscale
else:
raise RuntimeError(f'Unrecognized bounds for {self._cfg.env_id}: {bounds}')
if conditions is not None:
conditions /= scale
plt.clf()
fig = plt.gcf()
fig.set_size_inches(5, 5)
plt.imshow(self._background * .5,
extent=self._extent, cmap=plt.cm.binary, vmin=0, vmax=1)
path_length = len(observations)
colors = plt.cm.jet(np.linspace(0,1,path_length))
plt.plot(observations[:,1], observations[:,0], c='black', zorder=10)
plt.scatter(observations[:,1], observations[:,0], c=colors, zorder=20)
plt.axis('off')
plt.title(title)
img = plot2img(fig, remove_margins=self._remove_margins)
return img
def composite(self, savepath, paths, ncol=5, **kwargs):
assert len(paths) % ncol == 0, 'Number of paths must be divisible by number of columns'
images = []
for path, kw in zipkw(paths, **kwargs):
img = self.renders(*path, **kw)
images.append(img)
images = np.stack(images, axis=0)
nrow = len(images) // ncol
images = einops.rearrange(images,
'(nrow ncol) H W C -> (nrow H) (ncol W) C', nrow=nrow, ncol=ncol)
imageio.imsave(savepath, images)
print(f'Saved {len(paths)} samples to: {savepath}')
def _make_env(self, only_info=False):
return wrap_d4rl(
self._cfg.env_id,
norm_obs=self._cfg.get(
'norm_obs',
EasyDict(use_norm=False, offline_stats=dict(use_offline_stats=False, )),
),
norm_reward=self._cfg.get('norm_reward', EasyDict(use_norm=False, )),
only_info=only_info
)
def __repr__(self) -> str:
return "DI-engine D4RL Env({})".format(self._cfg.env_id)
@staticmethod
def create_collector_env_cfg(cfg: dict) -> List[dict]:
collector_cfg = copy.deepcopy(cfg)
collector_env_num = collector_cfg.pop('collector_env_num', 1)
return [collector_cfg for _ in range(collector_env_num)]
@staticmethod
def create_evaluator_env_cfg(cfg: dict) -> List[dict]:
evaluator_cfg = copy.deepcopy(cfg)
evaluator_env_num = evaluator_cfg.pop('evaluator_env_num', 1)
evaluator_cfg.get('norm_reward', EasyDict(use_norm=False, )).use_norm = False
return [evaluator_cfg for _ in range(evaluator_env_num)]
@property
def observation_space(self) -> gym.spaces.Space:
return self._observation_space
@property
def action_space(self) -> gym.spaces.Space:
return self._action_space
@property
def reward_space(self) -> gym.spaces.Space:
return self._reward_space
|