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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()
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