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import gym
from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator
from ding.model import VAC
from ding.policy import PPOPolicy
from ding.envs import DingEnvWrapper, EvalEpisodeReturnWrapper, BaseEnvManager
from ding.config import compile_config
from ding.utils import set_pkg_seed
from dizoo.minigrid.config.minigrid_onppo_config import minigrid_ppo_config
from minigrid.wrappers import FlatObsWrapper
import numpy as np
from tensorboardX import SummaryWriter
import os
import gymnasium
class MinigridWrapper(gym.Wrapper):
def __init__(self, env):
super().__init__(env)
self._observation_space = gym.spaces.Box(low=float("-inf"), high=float("inf"), shape=(8, ), dtype=np.float32)
self._action_space = gym.spaces.Discrete(9)
self._action_space.seed(0) # default seed
self.reward_range = (float('-inf'), float('inf'))
self.max_steps = minigrid_ppo_config.env.max_step
def step(self, action):
obs, reward, done, _, info = self.env.step(action)
self.cur_step += 1
if self.cur_step > self.max_steps:
done = True
return obs, reward, done, info
def reset(self):
self.cur_step = 0
return self.env.reset()[0]
def wrapped_minigrid_env():
return DingEnvWrapper(
gymnasium.make(minigrid_ppo_config.env.env_id),
cfg={
'env_wrapper': [
lambda env: FlatObsWrapper(env),
lambda env: MinigridWrapper(env),
lambda env: EvalEpisodeReturnWrapper(env),
]
}
)
def main(cfg, seed=0, max_env_step=int(1e10), max_train_iter=int(1e10)):
cfg = compile_config(
cfg, BaseEnvManager, PPOPolicy, BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, 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_minigrid_env for _ in range(collector_env_num)], cfg=cfg.env.manager)
evaluator_env = BaseEnvManager(env_fn=[wrapped_minigrid_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/'.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
)
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)
learner.train(new_data, collector.envstep)
if collector.envstep >= max_env_step or learner.train_iter >= max_train_iter:
break
if __name__ == '__main__':
main(minigrid_ppo_config)
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