mirror of
https://github.com/wassname/Run-Skeleton-Run.git
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wassname
257 lines
9.1 KiB
Python
257 lines
9.1 KiB
Python
import argparse
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import os
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import json
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import copy
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import torch
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import torch.multiprocessing as mp
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from multiprocessing import Value
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from common.misc_util import boolean_flag, str2params, create_if_need
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from common.env_wrappers import create_env
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from common.torch_util import activations, hard_update
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from ddpg.model import create_model, create_act_update_fns, train_multi_thread, \
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train_single_thread, play_single_thread
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def parse_args():
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parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--seed', type=int, default=42)
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parser.add_argument('--difficulty', type=int, default=2)
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parser.add_argument('--max-obstacles', type=int, default=3)
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parser.add_argument('--logdir', type=str, default="./logs")
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parser.add_argument('--num-threads', type=int, default=1)
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parser.add_argument('--num-train-threads', type=int, default=1)
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boolean_flag(parser, "ddpg-wrapper", default=False)
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parser.add_argument('--skip-frames', type=int, default=1)
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parser.add_argument('--fail-reward', type=float, default=0.0)
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parser.add_argument('--reward-scale', type=float, default=1.)
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boolean_flag(parser, "flip-state-action", default=False)
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for agent in ["actor", "critic", "dynamics"]:
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parser.add_argument('--{}-layers'.format(agent), type=str, default="64-64")
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parser.add_argument('--{}-activation'.format(agent), type=str, default="relu")
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boolean_flag(parser, "{}-layer-norm".format(agent), default=False)
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boolean_flag(parser, "{}-parameters-noise".format(agent), default=False)
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boolean_flag(parser, "{}-parameters-noise-factorised".format(agent), default=False)
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parser.add_argument('--{}-lr'.format(agent), type=float, default=1e-3)
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parser.add_argument('--{}-lr-end'.format(agent), type=float, default=5e-5)
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parser.add_argument('--restore-{}-from'.format(agent), type=str, default=None)
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parser.add_argument('--gamma', type=float, default=0.96)
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parser.add_argument('--loss-type', type=str, default="quadric-linear")
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parser.add_argument('--grad-clip', type=float, default=10.)
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parser.add_argument('--tau', default=0.01, type=float)
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parser.add_argument('--train-steps', type=int, default=int(1e4))
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parser.add_argument('--batch-size', type=int, default=256) # per worker
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parser.add_argument('--buffer-size', type=int, default=int(1e6))
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boolean_flag(parser, "prioritized-replay", default=False)
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parser.add_argument('--prioritized-replay-alpha', default=0.6, type=float)
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parser.add_argument('--prioritized-replay-beta0', default=0.4, type=float)
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parser.add_argument('--initial-epsilon', default=1., type=float)
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parser.add_argument('--final-epsilon', default=0.01, type=float)
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parser.add_argument('--max-episodes', default=int(1e4), type=int)
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parser.add_argument('--max-update-steps', default=int(5e6), type=int)
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parser.add_argument('--epsilon-cycle-len', default=int(2e2), type=int)
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parser.add_argument('--max-train-days', default=int(1e1), type=int)
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parser.add_argument('--rp-type', default="ornstein-uhlenbeck", type=str)
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parser.add_argument('--rp-theta', default=0.15, type=float)
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parser.add_argument('--rp-sigma', default=0.2, type=float)
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parser.add_argument('--rp-sigma-min', default=0.15, type=float)
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parser.add_argument('--rp-mu', default=0.0, type=float)
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parser.add_argument('--clip-delta', type=int, default=10)
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parser.add_argument('--save-step', type=int, default=int(1e4))
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parser.add_argument('--restore-args-from', type=str, default=None)
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return parser.parse_args()
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def restore_args(args):
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with open(args.restore_args_from, "r") as fin:
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params = json.load(fin)
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del params["seed"]
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del params["difficulty"]
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del params["max_obstacles"]
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del params["logdir"]
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del params["num_threads"]
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del params["num_train_threads"]
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del params["skip_frames"]
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for agent in ["actor", "critic"]:
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del params["{}_lr".format(agent)]
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del params["{}_lr_end".format(agent)]
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del params["restore_{}_from".format(agent)]
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del params["grad_clip"]
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del params["tau"]
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del params["train_steps"]
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del params["batch_size"]
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del params["buffer_size"]
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del params["prioritized_replay"]
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del params["prioritized_replay_alpha"]
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del params["prioritized_replay_beta0"]
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del params["initial_epsilon"]
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del params["final_epsilon"]
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del params["max_episodes"]
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del params["max_update_steps"]
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del params["epsilon_cycle_len"]
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del params["max_train_days"]
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del params["rp_type"]
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del params["rp_theta"]
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del params["rp_sigma"]
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del params["rp_sigma_min"]
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del params["rp_mu"]
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del params["clip_delta"]
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del params["save_step"]
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del params["restore_args_from"]
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for key, value in params.items():
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setattr(args, key, value)
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return args
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def train(args, model_fn, act_update_fns, multi_thread, train_single, play_single):
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create_if_need(args.logdir)
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if args.restore_args_from is not None:
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args = restore_args(args)
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with open("{}/args.json".format(args.logdir), "w") as fout:
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json.dump(vars(args), fout, indent=4, ensure_ascii=False, sort_keys=True)
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env = create_env(args)
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if args.flip_state_action and hasattr(env, "state_transform"):
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args.flip_states = env.state_transform.flip_states
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args.batch_size = args.batch_size // 2
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args.n_action = env.action_space.shape[0]
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args.n_observation = env.observation_space.shape[0]
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args.actor_layers = str2params(args.actor_layers)
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args.critic_layers = str2params(args.critic_layers)
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args.actor_activation = activations[args.actor_activation]
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args.critic_activation = activations[args.critic_activation]
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actor, critic, dynamics = model_fn(args)
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if args.restore_actor_from is not None:
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actor.load_state_dict(torch.load(args.restore_actor_from))
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if args.restore_critic_from is not None:
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critic.load_state_dict(torch.load(args.restore_critic_from))
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actor.train()
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critic.train()
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dynamics.train()
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actor.share_memory()
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critic.share_memory()
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dynamics.share_memory()
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target_actor = copy.deepcopy(actor)
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target_critic = copy.deepcopy(critic)
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target_dynamics = copy.deepcopy(dynamics)
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hard_update(target_actor, actor)
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hard_update(target_critic, critic)
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hard_update(target_dynamics, dynamics)
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target_actor.train()
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target_critic.train()
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target_dynamics.train()
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target_actor.share_memory()
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target_critic.share_memory()
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target_dynamics.share_memory()
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_, _, save_fn = act_update_fns(actor, critic, dynamics, target_actor, target_critic, target_dynamics, args)
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processes = []
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best_reward = Value("f", 0.0)
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try:
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if args.num_threads == args.num_train_threads == 1:
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# # run a single thread in the foreground so we can debug easier
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# args.thread = 1
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# multi_thread(actor, critic, dynamics, target_actor, target_critic, target_dynamics, args, act_update_fns, best_reward)
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# or debug the single thread funcs
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args.thread = 1
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global_episode = Value("i", 0)
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global_update_step = Value("i", 0)
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episodes_queue = mp.Queue()
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train_single(actor, critic, dynamics, target_actor, target_critic, target_dynamics, args, act_update_fns, global_episode, global_update_step, episodes_queue)
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elif args.num_threads == args.num_train_threads:
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for rank in range(args.num_threads):
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args.thread = rank
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p = mp.Process(
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target=multi_thread,
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args=(actor, critic, dynamics, target_actor, target_critic, target_dynamics, args, act_update_fns,
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best_reward))
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p.start()
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processes.append(p)
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else:
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global_episode = Value("i", 0)
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global_update_step = Value("i", 0)
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episodes_queue = mp.Queue()
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for rank in range(args.num_threads):
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args.thread = rank
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if rank < args.num_train_threads:
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p = mp.Process(
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target=train_single,
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args=(actor, critic, dynamics, target_actor, target_critic, target_dynamics, args, act_update_fns,
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global_episode, global_update_step, episodes_queue))
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else:
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p = mp.Process(
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target=play_single,
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args=(actor, critic, dynamics, target_actor, target_critic, target_dynamics, args, act_update_fns,
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global_episode, global_update_step, episodes_queue,
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best_reward))
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p.start()
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processes.append(p)
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for p in processes:
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p.join()
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except KeyboardInterrupt:
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pass
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save_fn()
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if __name__ == '__main__':
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os.environ['OMP_NUM_THREADS'] = '1'
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torch.set_num_threads(1)
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args = parse_args()
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train(args,
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create_model,
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create_act_update_fns,
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train_multi_thread,
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train_single_thread,
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play_single_thread)
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