def main()

in baselines/gail/run_mujoco.py [0:0]

• 46 lines of code
• 4 McCabe index (conditional complexity)

def main(args):
    U.make_session(num_cpu=1).__enter__()
    set_global_seeds(args.seed)
    env = gym.make(args.env_id)

    def policy_fn(name, ob_space, ac_space, reuse=False):
        return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
                                    reuse=reuse, hid_size=args.policy_hidden_size, num_hid_layers=2)
    env = bench.Monitor(env, logger.get_dir() and
                        osp.join(logger.get_dir(), "monitor.json"))
    env.seed(args.seed)
    gym.logger.setLevel(logging.WARN)
    task_name = get_task_name(args)
    args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
    args.log_dir = osp.join(args.log_dir, task_name)

    if args.task == 'train':
        dataset = Mujoco_Dset(expert_path=args.expert_path, traj_limitation=args.traj_limitation)
        reward_giver = TransitionClassifier(env, args.adversary_hidden_size, entcoeff=args.adversary_entcoeff)
        train(env,
              args.seed,
              policy_fn,
              reward_giver,
              dataset,
              args.algo,
              args.g_step,
              args.d_step,
              args.policy_entcoeff,
              args.num_timesteps,
              args.save_per_iter,
              args.checkpoint_dir,
              args.log_dir,
              args.pretrained,
              args.BC_max_iter,
              task_name
              )
    elif args.task == 'evaluate':
        runner(env,
               policy_fn,
               args.load_model_path,
               timesteps_per_batch=1024,
               number_trajs=10,
               stochastic_policy=args.stochastic_policy,
               save=args.save_sample
               )
    else:
        raise NotImplementedError
    env.close()