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import os
import gym
import torch
from tensorboardX import SummaryWriter
from easydict import EasyDict
from ding.config import compile_config
from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer
from ding.envs import BaseEnvManager, DingEnvWrapper
from ding.policy import DQNPolicy
from ding.model import DQN
from ding.utils import set_pkg_seed
from ding.rl_utils import get_epsilon_greedy_fn
from dizoo.classic_control.cartpole.config.cartpole_dqn_config import cartpole_dqn_config
# Get DI-engine form env class
def wrapped_cartpole_env():
return DingEnvWrapper(
gym.make('CartPole-v0'),
EasyDict(env_wrapper='default'),
)
# from dizoo.classic_control.cartpole.envs.cartpole_env import CartPoleEnv
# return CartPoleEnv({})
def main(cfg, seed=0):
cfg = compile_config(
cfg,
BaseEnvManager,
DQNPolicy,
BaseLearner,
SampleSerialCollector,
InteractionSerialEvaluator,
AdvancedReplayBuffer,
save_cfg=True
)
evaluator_env_num = cfg.env.evaluator_env_num
evaluator_env = BaseEnvManager(env_fn=[wrapped_cartpole_env for _ in range(evaluator_env_num)], cfg=cfg.env.manager)
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 = DQN(**cfg.policy.model)
policy = DQNPolicy(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(cartpole_dqn_config)
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