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import gym
from ditk import logging
from ding.model import DQN
from ding.policy import DQNPolicy
from ding.envs import DingEnvWrapper, SubprocessEnvManagerV2
from ding.envs.env_wrappers import MaxAndSkipWrapper, WarpFrameWrapper, ScaledFloatFrameWrapper, FrameStackWrapper, \
EvalEpisodeReturnWrapper, TimeLimitWrapper
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
from ding.utils import set_pkg_seed
from mario_dqn_config import main_config, create_config
import gym_super_mario_bros
from nes_py.wrappers import JoypadSpace
def wrapped_mario_env():
return DingEnvWrapper(
JoypadSpace(gym_super_mario_bros.make("SuperMarioBros-1-1-v0"), [["right"], ["right", "A"]]),
cfg={
'env_wrapper': [
lambda env: MaxAndSkipWrapper(env, skip=4),
lambda env: WarpFrameWrapper(env, size=84),
lambda env: ScaledFloatFrameWrapper(env),
lambda env: FrameStackWrapper(env, n_frames=4),
lambda env: TimeLimitWrapper(env, max_limit=400),
lambda env: EvalEpisodeReturnWrapper(env),
]
}
)
def main():
filename = '{}/log.txt'.format(main_config.exp_name)
logging.getLogger(with_files=[filename]).setLevel(logging.INFO)
cfg = compile_config(main_config, create_cfg=create_config, auto=True)
with task.start(async_mode=False, ctx=OnlineRLContext()):
collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num
collector_env = SubprocessEnvManagerV2(
env_fn=[wrapped_mario_env for _ in range(collector_env_num)], cfg=cfg.env.manager
)
evaluator_env = SubprocessEnvManagerV2(
env_fn=[wrapped_mario_env for _ in range(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=1000))
task.run()
if __name__ == "__main__":
main()
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