e8f8cdfa41
- fix a bug in #427: logger.write should pass a dict - change SubprocVectorEnv to ShmemVectorEnv in atari - increase logger interval for eps
254 lines
9.0 KiB
Python
254 lines
9.0 KiB
Python
import argparse
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import datetime
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import os
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import pprint
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import numpy as np
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import torch
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from atari_network import Rainbow
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from atari_wrapper import wrap_deepmind
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.data import Collector, PrioritizedVectorReplayBuffer, VectorReplayBuffer
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from tianshou.env import ShmemVectorEnv
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from tianshou.policy import RainbowPolicy
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from tianshou.trainer import offpolicy_trainer
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from tianshou.utils import TensorboardLogger
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--task', type=str, default='PongNoFrameskip-v4')
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parser.add_argument('--seed', type=int, default=0)
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parser.add_argument('--eps-test', type=float, default=0.005)
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parser.add_argument('--eps-train', type=float, default=1.)
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parser.add_argument('--eps-train-final', type=float, default=0.05)
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parser.add_argument('--buffer-size', type=int, default=100000)
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parser.add_argument('--lr', type=float, default=0.0000625)
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parser.add_argument('--gamma', type=float, default=0.99)
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parser.add_argument('--num-atoms', type=int, default=51)
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parser.add_argument('--v-min', type=float, default=-10.)
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parser.add_argument('--v-max', type=float, default=10.)
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parser.add_argument('--noisy-std', type=float, default=0.1)
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parser.add_argument('--no-dueling', action='store_true', default=False)
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parser.add_argument('--no-noisy', action='store_true', default=False)
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parser.add_argument('--no-priority', action='store_true', default=False)
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parser.add_argument('--alpha', type=float, default=0.5)
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parser.add_argument('--beta', type=float, default=0.4)
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parser.add_argument('--beta-final', type=float, default=1.)
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parser.add_argument('--beta-anneal-step', type=int, default=5000000)
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parser.add_argument('--no-weight-norm', action='store_true', default=False)
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parser.add_argument('--n-step', type=int, default=3)
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parser.add_argument('--target-update-freq', type=int, default=500)
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parser.add_argument('--epoch', type=int, default=100)
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parser.add_argument('--step-per-epoch', type=int, default=100000)
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parser.add_argument('--step-per-collect', type=int, default=10)
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parser.add_argument('--update-per-step', type=float, default=0.1)
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parser.add_argument('--batch-size', type=int, default=32)
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parser.add_argument('--training-num', type=int, default=10)
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parser.add_argument('--test-num', type=int, default=10)
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parser.add_argument('--logdir', type=str, default='log')
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parser.add_argument('--render', type=float, default=0.)
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parser.add_argument(
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'--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu'
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)
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parser.add_argument('--frames-stack', type=int, default=4)
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parser.add_argument('--resume-path', type=str, default=None)
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parser.add_argument(
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'--watch',
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default=False,
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action='store_true',
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help='watch the play of pre-trained policy only'
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)
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parser.add_argument('--save-buffer-name', type=str, default=None)
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return parser.parse_args()
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def make_atari_env(args):
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return wrap_deepmind(args.task, frame_stack=args.frames_stack)
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def make_atari_env_watch(args):
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return wrap_deepmind(
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args.task,
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frame_stack=args.frames_stack,
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episode_life=False,
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clip_rewards=False
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)
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def test_rainbow(args=get_args()):
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env = make_atari_env(args)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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# should be N_FRAMES x H x W
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print("Observations shape:", args.state_shape)
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print("Actions shape:", args.action_shape)
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# make environments
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train_envs = ShmemVectorEnv(
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[lambda: make_atari_env(args) for _ in range(args.training_num)]
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)
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test_envs = ShmemVectorEnv(
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[lambda: make_atari_env_watch(args) for _ in range(args.test_num)]
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)
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# seed
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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train_envs.seed(args.seed)
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test_envs.seed(args.seed)
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# define model
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net = Rainbow(
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*args.state_shape,
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args.action_shape,
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args.num_atoms,
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args.noisy_std,
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args.device,
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is_dueling=not args.no_dueling,
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is_noisy=not args.no_noisy
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)
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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# define policy
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policy = RainbowPolicy(
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net,
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optim,
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args.gamma,
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args.num_atoms,
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args.v_min,
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args.v_max,
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args.n_step,
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target_update_freq=args.target_update_freq
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).to(args.device)
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# load a previous policy
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if args.resume_path:
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policy.load_state_dict(torch.load(args.resume_path, map_location=args.device))
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print("Loaded agent from: ", args.resume_path)
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# replay buffer: `save_last_obs` and `stack_num` can be removed together
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# when you have enough RAM
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if args.no_priority:
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buffer = VectorReplayBuffer(
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args.buffer_size,
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buffer_num=len(train_envs),
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ignore_obs_next=True,
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save_only_last_obs=True,
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stack_num=args.frames_stack
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)
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else:
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buffer = PrioritizedVectorReplayBuffer(
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args.buffer_size,
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buffer_num=len(train_envs),
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ignore_obs_next=True,
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save_only_last_obs=True,
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stack_num=args.frames_stack,
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alpha=args.alpha,
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beta=args.beta,
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weight_norm=not args.no_weight_norm
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)
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# collector
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train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
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test_collector = Collector(policy, test_envs, exploration_noise=True)
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# log
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log_path = os.path.join(
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args.logdir, args.task, 'rainbow',
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f'seed_{args.seed}_{datetime.datetime.now().strftime("%m%d-%H%M%S")}'
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)
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writer = SummaryWriter(log_path)
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writer.add_text("args", str(args))
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logger = TensorboardLogger(writer)
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def save_fn(policy):
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torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
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def stop_fn(mean_rewards):
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if env.spec.reward_threshold:
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return mean_rewards >= env.spec.reward_threshold
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elif 'Pong' in args.task:
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return mean_rewards >= 20
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else:
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return False
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def train_fn(epoch, env_step):
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# nature DQN setting, linear decay in the first 1M steps
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if env_step <= 1e6:
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eps = args.eps_train - env_step / 1e6 * \
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(args.eps_train - args.eps_train_final)
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else:
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eps = args.eps_train_final
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policy.set_eps(eps)
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if env_step % 1000 == 0:
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logger.write("train/env_step", env_step, {"train/eps": eps})
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if not args.no_priority:
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if env_step <= args.beta_anneal_step:
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beta = args.beta - env_step / args.beta_anneal_step * \
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(args.beta - args.beta_final)
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else:
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beta = args.beta_final
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buffer.set_beta(beta)
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if env_step % 1000 == 0:
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logger.write("train/env_step", env_step, {"train/beta": beta})
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def test_fn(epoch, env_step):
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policy.set_eps(args.eps_test)
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# watch agent's performance
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def watch():
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print("Setup test envs ...")
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policy.eval()
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policy.set_eps(args.eps_test)
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test_envs.seed(args.seed)
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if args.save_buffer_name:
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print(f"Generate buffer with size {args.buffer_size}")
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buffer = PrioritizedVectorReplayBuffer(
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args.buffer_size,
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buffer_num=len(test_envs),
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ignore_obs_next=True,
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save_only_last_obs=True,
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stack_num=args.frames_stack,
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alpha=args.alpha,
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beta=args.beta
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)
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collector = Collector(policy, test_envs, buffer, exploration_noise=True)
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result = collector.collect(n_step=args.buffer_size)
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print(f"Save buffer into {args.save_buffer_name}")
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# Unfortunately, pickle will cause oom with 1M buffer size
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buffer.save_hdf5(args.save_buffer_name)
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else:
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print("Testing agent ...")
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test_collector.reset()
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result = test_collector.collect(
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n_episode=args.test_num, render=args.render
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)
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rew = result["rews"].mean()
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print(f'Mean reward (over {result["n/ep"]} episodes): {rew}')
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if args.watch:
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watch()
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exit(0)
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# test train_collector and start filling replay buffer
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train_collector.collect(n_step=args.batch_size * args.training_num)
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# trainer
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result = offpolicy_trainer(
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policy,
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train_collector,
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test_collector,
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args.epoch,
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args.step_per_epoch,
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args.step_per_collect,
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args.test_num,
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args.batch_size,
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train_fn=train_fn,
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test_fn=test_fn,
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stop_fn=stop_fn,
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save_fn=save_fn,
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logger=logger,
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update_per_step=args.update_per_step,
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test_in_train=False
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)
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pprint.pprint(result)
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watch()
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if __name__ == '__main__':
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test_rainbow(get_args())
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