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import os
import gym
from tensorboardX import SummaryWriter
from ding.config import compile_config
from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, NaiveReplayBuffer
from ding.envs import BaseEnvManager, DingEnvWrapper
from ding.policy import PPOPolicy
from ding.model import VAC
from ding.utils import set_pkg_seed
from dizoo.classic_control.pendulum.envs import PendulumEnv
from dizoo.classic_control.pendulum.config.pendulum_ppo_config import pendulum_ppo_config
def main(cfg, seed=0, max_iterations=int(1e10)):
cfg = compile_config(
cfg,
BaseEnvManager,
PPOPolicy,
BaseLearner,
SampleSerialCollector,
InteractionSerialEvaluator,
NaiveReplayBuffer,
save_cfg=True
)
collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num
collector_env = BaseEnvManager(
env_fn=[lambda: PendulumEnv(cfg=cfg.env) for _ in range(collector_env_num)], cfg=cfg.env.manager
)
evaluator_env = BaseEnvManager(
env_fn=[lambda: PendulumEnv(cfg=cfg.env) for _ in range(evaluator_env_num)], cfg=cfg.env.manager
)
collector_env.seed(seed)
evaluator_env.seed(seed, dynamic_seed=False)
set_pkg_seed(seed, use_cuda=cfg.policy.cuda)
model = VAC(**cfg.policy.model)
policy = PPOPolicy(cfg.policy, model=model)
tb_logger = SummaryWriter(os.path.join('./log/', 'serial'))
learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger)
collector = SampleSerialCollector(cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger)
evaluator = InteractionSerialEvaluator(cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger)
for _ in range(max_iterations):
if evaluator.should_eval(learner.train_iter):
stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
if stop:
break
new_data = collector.collect(train_iter=learner.train_iter)
learner.train(new_data, collector.envstep)
if __name__ == "__main__":
main(pendulum_ppo_config)
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