def make_env(config, writer, prefix, datadir, store):
suite, task = config.task.split('_', 1)
if suite == 'dmc':
env = wrappers.DeepMindControl(task)
env = wrappers.ActionRepeat(env, config.action_repeat)
env = wrappers.NormalizeActions(env)
elif suite == 'gym':
env = wrappers.Gym(task, config, size=(128, 128))
env = wrappers.ActionRepeat(env, config.action_repeat)
env = wrappers.NormalizeActions(env)
elif task == 'door':
env = wrappers.DoorOpen(config, size=(128, 128))
env = wrappers.ActionRepeat(env, config.action_repeat)
env = wrappers.NormalizeActions(env)
elif task == 'drawer':
env = wrappers.DrawerOpen(config, size=(128, 128))
env = wrappers.ActionRepeat(env, config.action_repeat)
env = wrappers.NormalizeActions(env)
else:
raise NotImplementedError(suite)
env = wrappers.TimeLimit(env, config.time_limit / config.action_repeat)
callbacks = []
if store:
callbacks.append(lambda ep: tools.save_episodes(datadir, [ep]))
if prefix == 'test':
callbacks.append(
lambda ep: summarize_episode(ep, config, datadir, writer, prefix))
env = wrappers.Collect(env, callbacks, config.precision)
env = wrappers.RewardObs(env)
return env
def make_env(config, writer, prefix, datadir, store):
if "lunar" in config.task:
env = wrappers.LunarLander(size=(64, 64),
action_repeat=config.action_repeat)
elif "Car" in config.task:
env = wrappers.CarEnvWrapper(env_name=config.task,
size=(64, 64),
action_repeat=config.action_repeat,
seed=config.seed)
else:
suite, task = config.task.split('_', 1)
if suite == 'dmc':
env = wrappers.DeepMindControl(task)
env = wrappers.ActionRepeat(env, config.action_repeat)
env = wrappers.NormalizeActions(env)
elif suite == 'atari':
env = wrappers.Atari(task,
config.action_repeat, (64, 64),
grayscale=False,
life_done=True,
sticky_actions=True)
env = wrappers.OneHotAction(env)
else:
raise NotImplementedError(suite)
env = wrappers.TimeLimit(env, config.time_limit / config.action_repeat)
callbacks = []
if store:
callbacks.append(lambda ep: tools.save_episodes(datadir, [ep]))
callbacks.append(
lambda ep: summarize_episode(ep, config, datadir, writer, prefix))
env = wrappers.Collect(env, callbacks, config.precision)
env = wrappers.RewardObs(env)
return env
def make_env(config, writer, prefix, datadir, store):
suite, task = config.task.split("_", 1)
if suite == "dmc":
env = wrappers.DeepMindControl(task)
env = wrappers.ActionRepeat(env, config.action_repeat)
env = wrappers.NormalizeActions(env)
elif suite == "atari":
env = wrappers.Atari(
task,
config.action_repeat,
(64, 64),
grayscale=False,
life_done=True,
sticky_actions=True,
)
env = wrappers.OneHotAction(env)
else:
raise NotImplementedError(suite)
env = wrappers.TimeLimit(env, config.time_limit / config.action_repeat)
callbacks = []
if store:
callbacks.append(lambda ep: tools.save_episodes(datadir, [ep]))
callbacks.append(lambda ep: summarize_episode(ep, config, datadir, writer, prefix))
env = wrappers.Collect(env, callbacks, config.precision)
env = wrappers.RewardObs(env)
return env
def make_initialization_env(config):
env = make_single_track_env('columbia',
action_repeat=config.action_repeat,
rendering=False)
datadir = pathlib.Path('.tmp')
writer = tf.summary.create_file_writer(str(datadir),
max_queue=1000,
flush_millis=20000)
callbacks_list = []
callbacks_list.append(
lambda episodes: callbacks.save_episodes(datadir, episodes))
env = wrappers.Collect(env, callbacks_list, config.precision)
return writer, datadir, env
def make_test_env(config, writer, datadir, gui=False):
env = make_base_env(config, gui)
env = wrappers.FixedResetMode(env, mode='grid')
env = wrappers.TimeLimit(env,
config.time_limit_test / config.action_repeat)
# rendering
render_callbacks = []
render_callbacks.append(
lambda videos: callbacks.save_videos(videos, config, datadir))
env = wrappers.Render(env, render_callbacks)
# summary
callback_list = []
callback_list.append(lambda episodes: callbacks.summarize_episode(
episodes, config, datadir, writer, 'test'))
env = wrappers.Collect(env, callback_list, config.precision)
return env
def make_env(config,
writer,
prefix,
datadir,
store,
index=None,
real_world=False):
suite, task = config.task.split('_', 1)
if suite == 'dmc':
if config.dr is None or real_world: #first index is always real world
env = wrappers.DeepMindControl(task,
use_state=config.use_state,
real_world=real_world)
else:
env = wrappers.DeepMindControl(task,
dr=config.dr,
use_state=config.use_state,
real_world=real_world)
env = wrappers.ActionRepeat(env, config.action_repeat)
env = wrappers.NormalizeActions(env)
elif suite == 'atari':
env = wrappers.Atari(task,
config.action_repeat, (64, 64),
grayscale=False,
life_done=True,
sticky_actions=True)
env = wrappers.OneHotAction(env)
elif suite == 'gym':
if index == 0 or index is None: #first index is always real world
env = wrappers.GymControl(task)
else:
env = wrappers.GymControl(task, dr=config.dr)
env = wrappers.ActionRepeat(env, config.action_repeat)
env = wrappers.NormalizeActions(env)
else:
raise NotImplementedError(suite)
env = wrappers.TimeLimit(env, config.time_limit / config.action_repeat)
callbacks = []
if store:
callbacks.append(lambda ep: tools.save_episodes(datadir, [ep]))
callbacks.append(
lambda ep: summarize_episode(ep, config, datadir, writer, prefix))
env = wrappers.Collect(env, callbacks, config.precision)
env = wrappers.RewardObs(env)
return env
def make_train_env(config, writer, datadir, gui=False):
env = make_base_env(config, gui)
if env.n_agents > 1:
env = wrappers.FixedResetMode(
env,
mode='random_ball') # sample in random points close within a ball
else:
env = wrappers.FixedResetMode(env, mode='random')
env = wrappers.TimeLimit(env,
config.time_limit_train / config.action_repeat)
# storing and summary
callback_list = []
callback_list.append(
lambda episodes: callbacks.save_episodes(datadir, episodes))
callback_list.append(lambda episodes: callbacks.summarize_episode(
episodes, config, datadir, writer, 'train'))
env = wrappers.Collect(env, callback_list, config.precision)
return env
def make_gridworld_env(config, writer, prefix, datadir, store, desc=None):
##NOTE:CHANEG:new function
# dreamer设计的env接口长什么样
suite, task = config.task.split('_', 1)
if suite == 'gridworld':
env = wrappers.GridWorld(desc)
env = wrappers.OneHotAction(env)
else:
raise NotImplementedError(suite)
env = wrappers.TimeLimit(env, config.time_limit / config.action_repeat)
callbacks = []
if store:
callbacks.append(lambda ep: tools.save_episodes(datadir, [ep]))
callbacks.append(
lambda ep: summarize_episode(ep, config, datadir, writer, prefix))
env = wrappers.Collect(env, callbacks, config.precision)
# collect here
env = wrappers.RewardObs(env)
return env
def wrap_wrt_track(env,
action_repeat,
outdir,
writer,
track,
checkpoint_id,
save_trajectories=False):
env = wrappers.OccupancyMapObs(env)
render_callbacks = []
render_callbacks.append(lambda videos: save_eval_videos(
videos, outdir / 'videos', action_repeat, track, checkpoint_id))
env = wrappers.Render(env, render_callbacks, follow_view=False)
callbacks = []
if save_trajectories:
callbacks.append(lambda episodes: save_trajectory(
episodes, outdir, action_repeat, track, checkpoint_id))
callbacks.append(lambda episodes: summarize_eval_episode(
episodes, outdir, writer, f'{track}', action_repeat))
env = wrappers.Collect(env, callbacks)
return env
def make_env(config, writer, prefix, datadir, store):
suite, task = config.task.split('_', 1)
if suite == 'dmc':
env = wrappers.DeepMindControl(task)
env = wrappers.ActionRepeat(env, config.action_repeat)
env = wrappers.NormalizeActions(env)
elif suite == 'atari':
env = wrappers.Atari(task,
config.action_repeat, (64, 64),
grayscale=False,
life_done=True,
sticky_actions=True)
env = wrappers.OneHotAction(env)
elif suite == 'football':
env = football_env.create_environment(
representation='pixels',
env_name='academy_empty_goal_close',
stacked=False,
logdir='./football/empty_goal_close2',
write_goal_dumps=True,
write_full_episode_dumps=True,
render=True,
write_video=True)
env = wrappers.Football(env)
env = wrappers.OneHotAction(env)
else:
raise NotImplementedError(suite)
env = wrappers.TimeLimit(env, config.time_limit / config.action_repeat)
callbacks = []
if store:
callbacks.append(lambda ep: tools.save_episodes(datadir, [ep]))
callbacks.append(
lambda ep: summarize_episode(ep, config, datadir, writer, prefix))
env = wrappers.Collect(env, callbacks, config.precision)
env = wrappers.RewardObs(env)
return env
