def make_env(config, writer, prefix, datadir, store):
suite, task = config.task.split('_', 1)
if suite == 'kitchen':
env = wrappers.Kitchen2D(task)
env = wrappers.ActionRepeat(env, config.action_repeat)
env = wrappers.NormalizeActions(env)
elif 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]))
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):
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, logger, mode, train_eps, eval_eps):
suite, task = config.task.split('_', 1)
if suite == 'dmc':
env = wrappers.DeepMindControl(task, config.action_repeat, config.size)
env = wrappers.NormalizeActions(env)
elif suite == 'atari':
env = wrappers.Atari(task,
config.action_repeat,
config.size,
grayscale=config.grayscale,
life_done=False and (mode == 'train'),
sticky_actions=True,
all_actions=True)
env = wrappers.OneHotAction(env)
else:
raise NotImplementedError(suite)
env = wrappers.TimeLimit(env, config.time_limit)
env = wrappers.SelectAction(env, key='action')
callbacks = [
functools.partial(process_episode, config, logger, mode, train_eps,
eval_eps)
]
env = wrappers.CollectDataset(env, callbacks)
env = wrappers.RewardObs(env)
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_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
from environments.pendulum import UnderactuatedPendulum
# INITIALIZE COLORS FOR PROMPT ON WIN10 MACHINES
colorama.init()
# DATASET PARAMETERS
max_num_episodes = 120
len_time = 100
render_shape = (500, 500)
image_res = (28, 28)
# INITIALIZE PENDULUM ENVIRONMENT
env = gym.make('pendulum-underactuated-v0',
render_shape=render_shape,
model=None)
env = wrappers.NormalizeActions(env)
env = wrappers.MinimumDuration(env, len_time)
env = wrappers.MaximumDuration(env, len_time)
env = wrappers.ObservationDict(env, key='observation')
env = wrappers.PixelObservations(env, image_res, np.uint8, 'image')
env = wrappers.ConvertRewardToCost(env)
env = wrappers.ConvertTo32Bit(env)
# SEED EXPERIMENT TO CREATE REPRODUCIBLE RESULTS
seed_value = 0
seed_experiment(seed=seed_value)
env.seed(seed=seed_value)
# GET ENVIRONMENT DATA SHAPES
observation_shape = env.observation_space['image'].shape
action_shape = env.action_space.shape
