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 == '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):
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_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 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
import gym
import wrappers
import numpy as np
task = 'SingleAgentTreitlstrasse_v2-v0'
time_limit = 60 * 100
action_repeat = 8
env = gym.make(task)
env = wrappers.TimeLimit(env, time_limit)
env = wrappers.ActionRepeat(env, action_repeat)
def test_on_track(model, outdir):
video, returns = simulate_episode(model)
videodir = outdir / 'videos'
videodir.mkdir(parents=True, exist_ok=True)
import imageio
writer = imageio.get_writer(videodir / f'test_return{returns}.mp4')
for image in video:
writer.append_data(image)
writer.close()
def simulate_episode(model, prediction_window=5, terminate_on_collision=True):
# to do: make it uniform to f1_tenth directory
done = False
obs = env.reset(mode='grid')
state = None
video = []
returns = 0.0
while not done:
