File size: 4,111 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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
import os
from functools import partial
import gym
import numpy as np
from easydict import EasyDict
from tensorboardX import SummaryWriter
from ding.torch_utils import to_ndarray
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.procgen.config.coinrun_ppo_config import coinrun_ppo_config
class CoinrunWrapper(gym.Wrapper):
def __init__(self, env, cfg):
super().__init__(env)
cfg = EasyDict(cfg)
self._cfg = cfg
self._observation_space = gym.spaces.Box(
low=np.zeros(shape=(3, 64, 64)), high=np.ones(shape=(3, 64, 64)) * 255, shape=(3, 64, 64), dtype=np.float32
)
self._action_space = gym.spaces.Discrete(15)
self._reward_space = gym.spaces.Box(low=float("-inf"), high=float("inf"), shape=(1, ), dtype=np.float32)
def _process_obs(self, obs):
obs = to_ndarray(obs)
obs = np.transpose(obs, (2, 0, 1))
obs = obs.astype(np.float32)
return obs
def step(self, action):
obs, reward, done, info = self.env.step(action)
return self._process_obs(obs), reward, bool(done), info
def reset(self):
obs = self.env.reset()
return self._process_obs(obs)
def wrapped_procgen_env(cfg):
default_cfg = dict(
control_level=True,
start_level=0,
num_levels=0,
env_id='coinrun',
)
default_cfg.update(cfg)
default_cfg = EasyDict(default_cfg)
return DingEnvWrapper(
gym.make(
'procgen:procgen-' + default_cfg.env_id + '-v0',
start_level=default_cfg.start_level,
num_levels=default_cfg.num_levels
) if default_cfg.control_level else
gym.make('procgen:procgen-' + default_cfg.env_id + '-v0', start_level=0, num_levels=1),
cfg={
'env_wrapper': [
lambda env: CoinrunWrapper(env, default_cfg),
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=[partial(wrapped_procgen_env, cfg=coinrun_ppo_config.env) for _ in range(collector_env_num)],
cfg=cfg.env.manager
)
evaluator_env = BaseEnvManager(
env_fn=[partial(wrapped_procgen_env, cfg=coinrun_ppo_config.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(coinrun_ppo_config)
|