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import os
from easydict import EasyDict
from tensorboardX import SummaryWriter
from ding.config import compile_config
from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer
from ding.envs.env_manager.envpool_env_manager import PoolEnvManager
from ding.policy import DQNPolicy
from ding.model import DQN
from ding.utils import set_pkg_seed
from ding.rl_utils import get_epsilon_greedy_fn
from dizoo.atari.config.serial import pong_dqn_envpool_config
def main(cfg, seed=0, max_iterations=int(1e10)):
cfg.exp_name = 'atari_dqn_envpool'
cfg = compile_config(
cfg,
PoolEnvManager,
DQNPolicy,
BaseLearner,
SampleSerialCollector,
InteractionSerialEvaluator,
AdvancedReplayBuffer,
save_cfg=True
)
collector_env_cfg = EasyDict(
{
'env_id': cfg.env.env_id,
'env_num': cfg.env.collector_env_num,
'batch_size': cfg.env.collector_batch_size,
# env wrappers
'episodic_life': True, # collector: True
'reward_clip': True, # collector: True
'gray_scale': cfg.env.get('gray_scale', True),
'stack_num': cfg.env.get('stack_num', 4),
'frame_skip': cfg.env.get('frame_skip', 4),
}
)
collector_env = PoolEnvManager(collector_env_cfg)
evaluator_env_cfg = EasyDict(
{
'env_id': cfg.env.env_id,
'env_num': cfg.env.evaluator_env_num,
'batch_size': cfg.env.evaluator_batch_size,
# env wrappers
'episodic_life': False, # evaluator: False
'reward_clip': False, # evaluator: False
'gray_scale': cfg.env.get('gray_scale', True),
'stack_num': cfg.env.get('stack_num', 4),
'frame_skip': cfg.env.get('frame_skip', 4),
}
)
evaluator_env = PoolEnvManager(evaluator_env_cfg)
collector_env.seed(seed)
evaluator_env.seed(seed)
set_pkg_seed(seed, use_cuda=cfg.policy.cuda)
model = DQN(**cfg.policy.model)
policy = DQNPolicy(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
)
replay_buffer = AdvancedReplayBuffer(
cfg.policy.other.replay_buffer, tb_logger, exp_name=cfg.exp_name, instance_name='replay_buffer'
)
eps_cfg = cfg.policy.other.eps
epsilon_greedy = get_epsilon_greedy_fn(eps_cfg.start, eps_cfg.end, eps_cfg.decay, eps_cfg.type)
while True:
if evaluator.should_eval(learner.train_iter):
stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
if stop:
break
eps = epsilon_greedy(collector.envstep)
new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs={'eps': eps})
replay_buffer.push(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')
train_data = replay_buffer.sample(batch_size, learner.train_iter)
if train_data is not None:
learner.train(train_data, collector.envstep)
if __name__ == "__main__":
main(EasyDict(pong_dqn_envpool_config))
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