File size: 4,030 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 |
import os
import torch
import gym
import numpy as np
from tensorboardX import SummaryWriter
from rocket_recycling.rocket import Rocket
from ditk import logging
from ding.model import VAC
from ding.policy import PPOPolicy
from ding.envs import DingEnvWrapper, BaseEnvManagerV2, EvalEpisodeReturnWrapper
from ding.config import compile_config
from ding.framework import task
from ding.framework.context import OnlineRLContext
from ding.framework.middleware import multistep_trainer, StepCollector, interaction_evaluator, CkptSaver, \
gae_estimator, termination_checker
from ding.utils import set_pkg_seed
from dizoo.rocket.config.rocket_landing_ppo_config import main_config, create_config
class RocketLandingWrapper(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'))
def wrapped_rocket_env(task, max_steps):
return DingEnvWrapper(
Rocket(task=task, max_steps=max_steps),
cfg={'env_wrapper': [
lambda env: RocketLandingWrapper(env),
lambda env: EvalEpisodeReturnWrapper(env),
]}
)
def main():
logging.getLogger().setLevel(logging.INFO)
main_config.exp_name = 'rocket_landing_ppo_nseed'
main_config.policy.cuda = True
print('torch.cuda.is_available(): ', torch.cuda.is_available())
cfg = compile_config(main_config, create_cfg=create_config, auto=True)
num_seed = 4
for seed_i in range(num_seed):
tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'seed' + str(seed_i)))
with task.start(async_mode=False, ctx=OnlineRLContext()):
collector_env = BaseEnvManagerV2(
env_fn=[
lambda: wrapped_rocket_env(cfg.env.task, cfg.env.max_steps)
for _ in range(cfg.env.collector_env_num)
],
cfg=cfg.env.manager
)
evaluator_env = BaseEnvManagerV2(
env_fn=[
lambda: wrapped_rocket_env(cfg.env.task, cfg.env.max_steps)
for _ in range(cfg.env.evaluator_env_num)
],
cfg=cfg.env.manager
)
# evaluator_env.enable_save_replay()
set_pkg_seed(seed_i, use_cuda=cfg.policy.cuda)
model = VAC(**cfg.policy.model)
policy = PPOPolicy(cfg.policy, model=model)
def _add_scalar(ctx):
if ctx.eval_value != -np.inf:
tb_logger.add_scalar('evaluator_step/reward', ctx.eval_value, global_step=ctx.env_step)
collector_rewards = [ctx.trajectories[i]['reward'] for i in range(len(ctx.trajectories))]
collector_mean_reward = sum(collector_rewards) / len(ctx.trajectories)
collector_max_reward = max(collector_rewards)
collector_min_reward = min(collector_rewards)
tb_logger.add_scalar('collecter_step/mean_reward', collector_mean_reward, global_step=ctx.env_step)
tb_logger.add_scalar('collecter_step/max_reward', collector_max_reward, global_step=ctx.env_step)
tb_logger.add_scalar('collecter_step/min_reward', collector_min_reward, global_step=ctx.env_step)
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))
task.use(multistep_trainer(cfg, policy.learn_mode))
task.use(CkptSaver(policy, cfg.exp_name, train_freq=100))
# task.use(_add_scalar)
task.use(termination_checker(max_env_step=int(3e6)))
task.run()
if __name__ == "__main__":
main()
|