|
from turtle import Terminator |
|
import gym |
|
from ditk import logging |
|
from ding.model import VAC |
|
from ding.policy import PPOPolicy |
|
from ding.envs import DingEnvWrapper, BaseEnvManagerV2 |
|
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 multistep_trainer, StepCollector, interaction_evaluator, CkptSaver, \ |
|
gae_estimator, termination_checker |
|
from ding.utils import set_pkg_seed |
|
from dizoo.rocket.envs.rocket_env import RocketEnv |
|
from dizoo.rocket.config.rocket_hover_ppo_config import main_config, create_config |
|
import numpy as np |
|
from tensorboardX import SummaryWriter |
|
import os |
|
import torch |
|
|
|
|
|
def main(): |
|
logging.getLogger().setLevel(logging.INFO) |
|
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 = 3 |
|
for seed_i in range(num_seed): |
|
main_config.exp_name = f'task_rocket_hovering_onppo_seed{seed_i}' |
|
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: RocketEnv(cfg.env) for _ in range(cfg.env.collector_env_num)], cfg=cfg.env.manager |
|
) |
|
evaluator_env = BaseEnvManagerV2( |
|
env_fn=[lambda: RocketEnv(cfg.env) for _ in range(cfg.env.evaluator_env_num)], cfg=cfg.env.manager |
|
) |
|
|
|
|
|
|
|
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(_add_scalar) |
|
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(termination_checker(max_env_step=int(10e7))) |
|
task.run() |
|
|
|
|
|
if __name__ == "__main__": |
|
main() |
|
|
