DI-engine/dizoo/mujoco/entry/mujoco_ddpg_eval.py

import os
import gym
import torch
from tensorboardX import SummaryWriter
from easydict import EasyDict
from functools import partial

from ding.config import compile_config
from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer
from ding.envs import BaseEnvManager
from ding.envs import get_vec_env_setting, create_env_manager
from ding.policy import DDPGPolicy
from ding.model import ContinuousQAC
from ding.utils import set_pkg_seed
from ding.rl_utils import get_epsilon_greedy_fn
from dizoo.mujoco.envs.mujoco_env import MujocoEnv
from dizoo.mujoco.config.ant_ddpg_config import ant_ddpg_config


def main(main_cfg, seed=0):
    cfg = compile_config(
        main_cfg,
        BaseEnvManager,
        DDPGPolicy,
        BaseLearner,
        SampleSerialCollector,
        InteractionSerialEvaluator,
        AdvancedReplayBuffer,
        MujocoEnv,
        save_cfg=True
    )

    # Create main components: env, policy
    env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env)
    evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg])

    evaluator_env.enable_save_replay(cfg.env.replay_path)  # switch save replay interface

    # Set random seed for all package and instance
    evaluator_env.seed(seed, dynamic_seed=False)
    set_pkg_seed(seed, use_cuda=cfg.policy.cuda)

    # Set up RL Policy
    model = ContinuousQAC(**cfg.policy.model)
    policy = DDPGPolicy(cfg.policy, model=model)
    policy.eval_mode.load_state_dict(torch.load(cfg.policy.load_path, map_location='cpu'))

    # evaluate
    tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
    evaluator = InteractionSerialEvaluator(
        cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
    )
    evaluator.eval()


if __name__ == "__main__":
    main(ant_ddpg_config, seed=0)