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import os
import gym
from tensorboardX import SummaryWriter
from easydict import EasyDict
from copy import deepcopy
from ding.config import compile_config
from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer
from ding.envs import BaseEnvManager, DingEnvWrapper
from ding.policy import PPGOffPolicy
from ding.model import PPG
from ding.utils import set_pkg_seed, deep_merge_dicts
from dizoo.classic_control.cartpole.config.cartpole_ppg_config import cartpole_ppg_config
def wrapped_cartpole_env():
return DingEnvWrapper(
gym.make('CartPole-v0'),
EasyDict(env_wrapper='default'),
)
def main(cfg, seed=0, max_train_iter=int(1e8), max_env_step=int(1e8)):
cfg = compile_config(
cfg,
BaseEnvManager,
PPGOffPolicy,
BaseLearner,
SampleSerialCollector,
InteractionSerialEvaluator, {
'policy': AdvancedReplayBuffer,
'value': AdvancedReplayBuffer
},
save_cfg=True
)
collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num
collector_env = BaseEnvManager(env_fn=[wrapped_cartpole_env for _ in range(collector_env_num)], cfg=cfg.env.manager)
evaluator_env = BaseEnvManager(env_fn=[wrapped_cartpole_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 = PPG(**cfg.policy.model)
policy = PPGOffPolicy(cfg.policy, model=model)
tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
collector = SampleSerialCollector(
cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name
)
evaluator = InteractionSerialEvaluator(
cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
)
policy_buffer = AdvancedReplayBuffer(
cfg.policy.other.replay_buffer.policy, tb_logger, exp_name=cfg.exp_name, instance_name='policy_buffer'
)
value_buffer = AdvancedReplayBuffer(
cfg.policy.other.replay_buffer.value, tb_logger, exp_name=cfg.exp_name, instance_name='value_buffer'
)
while True:
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)
policy_buffer.push(new_data, cur_collector_envstep=collector.envstep)
value_buffer.push(deepcopy(new_data), cur_collector_envstep=collector.envstep)
for i in range(cfg.policy.learn.update_per_collect):
batch_size = learner.policy.get_attribute('batch_size')
policy_data = policy_buffer.sample(batch_size['policy'], learner.train_iter)
value_data = value_buffer.sample(batch_size['value'], learner.train_iter)
if policy_data is not None and value_data is not None:
train_data = {'policy': policy_data, 'value': value_data}
learner.train(train_data, collector.envstep)
policy_buffer.clear()
value_buffer.clear()
if learner.train_iter >= max_train_iter or collector.envstep >= max_env_step:
break
if __name__ == "__main__":
main(cartpole_ppg_config)
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