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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, DingEnvWrapper
from ding.envs import get_vec_env_setting, create_env_manager
from ding.policy import PPOPolicy
from ding.model import VAC
from ding.utils import set_pkg_seed
from ding.rl_utils import get_epsilon_greedy_fn
from dizoo.box2d.bipedalwalker.config.bipedalwalker_ppo_config import main_config, create_config
def main(rl_cfg, seed=0):
main_cfg, create_cfg = rl_cfg
cfg = compile_config(
main_cfg,
BaseEnvManager,
PPOPolicy,
BaseLearner,
SampleSerialCollector,
InteractionSerialEvaluator,
AdvancedReplayBuffer,
create_cfg=create_cfg,
save_cfg=True
)
create_cfg.policy.type = create_cfg.policy.type + '_command'
env_fn = None
cfg = compile_config(cfg, seed=seed, env=env_fn, auto=True, create_cfg=create_cfg, 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 = VAC(**cfg.policy.model)
policy = PPOPolicy(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(rl_cfg=(main_config, create_config), seed=0)
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