zjowowen's picture
init space
079c32c
import gym
from ditk import logging
from ding.model import DQN
from ding.policy import DQNPolicy
from ding.envs import DingEnvWrapper, BaseEnvManagerV2
from ding.data import DequeBuffer
from ding.config import compile_config
from ding.framework import task
from ding.framework.context import OnlineRLContext
from ding.framework.middleware import OffPolicyLearner, StepCollector, interaction_evaluator, data_pusher, \
eps_greedy_handler, CkptSaver, nstep_reward_enhancer, final_ctx_saver
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_nstep'
main_config.policy.nstep = 3
cfg = compile_config(main_config, create_cfg=create_config, auto=True)
with task.start(async_mode=False, ctx=OnlineRLContext()):
collector_env = BaseEnvManagerV2(
env_fn=[lambda: DingEnvWrapper(gym.make("CartPole-v0")) for _ in range(cfg.env.collector_env_num)],
cfg=cfg.env.manager
)
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)
buffer_ = DequeBuffer(size=cfg.policy.other.replay_buffer.replay_buffer_size)
policy = DQNPolicy(cfg.policy, model=model)
task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env))
task.use(eps_greedy_handler(cfg))
task.use(StepCollector(cfg, policy.collect_mode, collector_env))
task.use(nstep_reward_enhancer(cfg))
task.use(data_pusher(cfg, buffer_))
task.use(OffPolicyLearner(cfg, policy.learn_mode, buffer_))
task.use(CkptSaver(policy, cfg.exp_name, train_freq=100))
task.use(final_ctx_saver(cfg.exp_name))
task.run()
if __name__ == "__main__":
main()