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from ditk import logging |
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import torch |
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from ding.model import ContinuousQAC |
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from ding.policy import SQILSACPolicy |
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from ding.envs import BaseEnvManagerV2 |
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from ding.data import DequeBuffer |
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from ding.config import compile_config |
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from ding.framework import task |
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from ding.framework.context import OnlineRLContext |
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from ding.framework.middleware import OffPolicyLearner, StepCollector, interaction_evaluator, \ |
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CkptSaver, sqil_data_pusher, termination_checker |
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from ding.utils import set_pkg_seed |
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from dizoo.classic_control.pendulum.envs.pendulum_env import PendulumEnv |
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from dizoo.classic_control.pendulum.config.pendulum_sac_config import main_config as ex_main_config |
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from dizoo.classic_control.pendulum.config.pendulum_sac_config import create_config as ex_create_config |
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from dizoo.classic_control.pendulum.config.pendulum_sqil_sac_config import main_config, create_config |
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def main(): |
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logging.getLogger().setLevel(logging.INFO) |
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cfg = compile_config(main_config, create_cfg=create_config, auto=True) |
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expert_cfg = compile_config(ex_main_config, create_cfg=ex_create_config, auto=True) |
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expert_cfg.policy.collect.n_sample = cfg.policy.collect.n_sample |
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with task.start(async_mode=False, ctx=OnlineRLContext()): |
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collector_env = BaseEnvManagerV2( |
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env_fn=[lambda: PendulumEnv(cfg.env) for _ in range(cfg.env.collector_env_num)], cfg=cfg.env.manager |
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) |
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expert_collector_env = BaseEnvManagerV2( |
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env_fn=[lambda: PendulumEnv(cfg.env) for _ in range(cfg.env.collector_env_num)], cfg=cfg.env.manager |
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) |
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evaluator_env = BaseEnvManagerV2( |
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env_fn=[lambda: PendulumEnv(cfg.env) for _ in range(cfg.env.evaluator_env_num)], cfg=cfg.env.manager |
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) |
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set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda) |
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model = ContinuousQAC(**cfg.policy.model) |
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expert_model = ContinuousQAC(**cfg.policy.model) |
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buffer_ = DequeBuffer(size=cfg.policy.other.replay_buffer.replay_buffer_size) |
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expert_buffer = DequeBuffer(size=cfg.policy.other.replay_buffer.replay_buffer_size) |
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policy = SQILSACPolicy(cfg.policy, model=model) |
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expert_policy = SQILSACPolicy(expert_cfg.policy, model=expert_model) |
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state_dict = torch.load(cfg.policy.collect.model_path, map_location='cpu') |
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expert_policy.collect_mode.load_state_dict(state_dict) |
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task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env)) |
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task.use( |
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StepCollector(cfg, policy.collect_mode, collector_env, random_collect_size=cfg.policy.random_collect_size) |
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) |
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task.use(sqil_data_pusher(cfg, buffer_, expert=False)) |
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task.use( |
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StepCollector( |
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cfg, |
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expert_policy.collect_mode, |
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expert_collector_env, |
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random_collect_size=cfg.policy.expert_random_collect_size |
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) |
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) |
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task.use(sqil_data_pusher(cfg, expert_buffer, expert=True)) |
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task.use(OffPolicyLearner(cfg, policy.learn_mode, [(buffer_, 0.5), (expert_buffer, 0.5)])) |
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task.use(CkptSaver(policy, cfg.exp_name, train_freq=100)) |
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task.use(termination_checker(max_train_iter=10000)) |
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task.run() |
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if __name__ == "__main__": |
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main() |
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