File size: 1,603 Bytes
079c32c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
import gym
import torch
from ditk import logging
from ding.model import DQN
from ding.policy import DQNPolicy
from ding.envs import DingEnvWrapper, BaseEnvManagerV2
from ding.config import compile_config
from ding.framework import task
from ding.framework.context import OnlineRLContext
from ding.framework.middleware import interaction_evaluator
from ding.utils import set_pkg_seed
from dizoo.classic_control.cartpole.config.cartpole_dqn_config import main_config, create_config
def main():
logging.getLogger().setLevel(logging.INFO)
main_config.exp_name = 'cartpole_dqn_eval'
cfg = compile_config(main_config, create_cfg=create_config, auto=True)
with task.start(async_mode=False, ctx=OnlineRLContext()):
evaluator_env = BaseEnvManagerV2(
env_fn=[lambda: DingEnvWrapper(gym.make("CartPole-v0")) for _ in range(cfg.env.evaluator_env_num)],
cfg=cfg.env.manager
)
set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
model = DQN(**cfg.policy.model)
# Load the pretrained weights.
# First, you should get a pretrained network weights.
# For example, you can run ``python3 -u ding/examples/dqn.py``.
pretrained_state_dict = torch.load('cartpole_dqn_seed0/ckpt/final.pth.tar', map_location='cpu')['model']
model.load_state_dict(pretrained_state_dict)
policy = DQNPolicy(cfg.policy, model=model)
# Define the evaluator middleware.
task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env))
task.run(max_step=1)
if __name__ == "__main__":
main()
|