File size: 3,135 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 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
import os
import gym
from tensorboardX import SummaryWriter
from easydict import EasyDict
from copy import deepcopy
from functools import partial
from ding.config import compile_config
from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer
from ding.envs import SyncSubprocessEnvManager
from ding.policy import DQNPolicy
from ding.model import DQN
from ding.utils import set_pkg_seed, deep_merge_dicts
from ding.rl_utils import get_epsilon_greedy_fn
from dizoo.atari.envs import AtariEnv
from dizoo.atari.config.serial.pong.pong_dqn_config import pong_dqn_config
def main(cfg, seed=0, max_iterations=int(1e10)):
cfg = compile_config(
cfg,
SyncSubprocessEnvManager,
DQNPolicy,
BaseLearner,
SampleSerialCollector,
InteractionSerialEvaluator,
AdvancedReplayBuffer,
save_cfg=True
)
collector_env_cfg = AtariEnv.create_collector_env_cfg(cfg.env)
evaluator_env_cfg = AtariEnv.create_evaluator_env_cfg(cfg.env)
collector_env = SyncSubprocessEnvManager(
env_fn=[partial(AtariEnv, cfg=c) for c in collector_env_cfg], cfg=cfg.env.manager
)
evaluator_env = SyncSubprocessEnvManager(
env_fn=[partial(AtariEnv, cfg=c) for c in evaluator_env_cfg], 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 = 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_config))
|