File size: 2,539 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 |
import gym
from ditk import logging
from ding.model import PPG
from ding.policy import PPGOffPolicy
from ding.envs import DingEnvWrapper, BaseEnvManagerV2
from ding.data import DequeBuffer
from ding.data.buffer.middleware import use_time_check, sample_range_view
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, \
CkptSaver, gae_estimator
from ding.utils import set_pkg_seed
from dizoo.classic_control.cartpole.config.cartpole_ppg_offpolicy_config import main_config, create_config
def main():
logging.getLogger().setLevel(logging.INFO)
cfg = compile_config(main_config, create_cfg=create_config, auto=True)
with task.start(async_mode=False, ctx=OnlineRLContext()):
collector_env = BaseEnvManagerV2(
env_fn=[lambda: DingEnvWrapper(gym.make("CartPole-v0")) for _ in range(cfg.env.collector_env_num)],
cfg=cfg.env.manager
)
evaluator_env = BaseEnvManagerV2(
env_fn=[lambda: DingEnvWrapper(gym.make("CartPole-v0")) for _ in range(cfg.env.evaluator_env_num)],
cfg=cfg.env.manager
)
set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
model = PPG(**cfg.policy.model)
buffer_cfg = cfg.policy.other.replay_buffer
max_size = max(buffer_cfg.policy.replay_buffer_size, buffer_cfg.value.replay_buffer_size)
buffer_ = DequeBuffer(size=max_size)
policy_buffer = buffer_.view() # shallow copy
policy_buffer.use(use_time_check(policy_buffer, max_use=buffer_cfg.policy.max_use))
policy_buffer.use(sample_range_view(policy_buffer, start=-buffer_cfg.policy.replay_buffer_size))
value_buffer = buffer_.view()
value_buffer.use(use_time_check(value_buffer, max_use=buffer_cfg.value.max_use))
value_buffer.use(sample_range_view(value_buffer, start=-buffer_cfg.value.replay_buffer_size))
policy = PPGOffPolicy(cfg.policy, model=model)
task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env))
task.use(StepCollector(cfg, policy.collect_mode, collector_env))
task.use(gae_estimator(cfg, policy.collect_mode, buffer_))
task.use(OffPolicyLearner(cfg, policy.learn_mode, {'policy': policy_buffer, 'value': value_buffer}))
task.use(CkptSaver(policy, cfg.exp_name, train_freq=100))
task.run()
if __name__ == "__main__":
main()
|