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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)
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