import gym from ditk import logging from ding.model import QACEnsemble from ding.policy import EDACPolicy from ding.envs import DingEnvWrapper, BaseEnvManagerV2 from ding.data import create_dataset from ding.config import compile_config from ding.framework import task, ding_init from ding.framework.context import OfflineRLContext from ding.framework.middleware import interaction_evaluator, trainer, CkptSaver, offline_data_fetcher, offline_logger from ding.utils import set_pkg_seed from dizoo.d4rl.envs import D4RLEnv from dizoo.d4rl.config.halfcheetah_medium_edac_config import main_config, create_config def main(): # If you don't have offline data, you need to prepare if first and set the data_path in config # For demostration, we also can train a RL policy (e.g. SAC) and collect some data logging.getLogger().setLevel(logging.INFO) cfg = compile_config(main_config, create_cfg=create_config, auto=True) ding_init(cfg) with task.start(async_mode=False, ctx=OfflineRLContext()): evaluator_env = BaseEnvManagerV2( env_fn=[lambda: D4RLEnv(cfg.env) for _ in range(cfg.env.evaluator_env_num)], cfg=cfg.env.manager ) set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda) dataset = create_dataset(cfg) model = QACEnsemble(**cfg.policy.model) policy = EDACPolicy(cfg.policy, model=model) task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env)) task.use(offline_data_fetcher(cfg, dataset)) task.use(trainer(cfg, policy.learn_mode)) task.use(CkptSaver(policy, cfg.exp_name, train_freq=1e4)) task.use(offline_logger()) task.run() if __name__ == "__main__": main()