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: