c6f2648e87
This is the PR for C51algorithm: https://arxiv.org/abs/1707.06887 1. add C51 policy in tianshou/policy/modelfree/c51.py. 2. add C51 net in tianshou/utils/net/discrete.py. 3. add C51 atari example in examples/atari/atari_c51.py. 4. add C51 statement in tianshou/policy/__init__.py. 5. add C51 test in test/discrete/test_c51.py. 6. add C51 atari results in examples/atari/results/c51/. By running "python3 atari_c51.py --task "PongNoFrameskip-v4" --batch-size 64", get best_result': '20.50 ± 0.50', in epoch 9. By running "python3 atari_c51.py --task "BreakoutNoFrameskip-v4" --n-step 1 --epoch 40", get best_reward: 407.400000 ± 31.155096 in epoch 39.
156 lines
5.9 KiB
Python
156 lines
5.9 KiB
Python
import os
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import torch
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import pprint
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import argparse
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import numpy as np
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.policy import C51Policy
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from tianshou.env import SubprocVectorEnv
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from tianshou.utils.net.discrete import C51
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from tianshou.trainer import offpolicy_trainer
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from tianshou.data import Collector, ReplayBuffer
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from atari_wrapper import wrap_deepmind
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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=0.0001)
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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('--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=10000)
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parser.add_argument('--collect-per-step', type=int, default=10)
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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=16)
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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,
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default='cuda' if torch.cuda.is_available() else 'cpu')
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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('--watch', default=False, action='store_true',
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help='watch the play of pre-trained policy only')
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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(args.task, frame_stack=args.frames_stack,
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episode_life=False, clip_rewards=False)
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def test_c51(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.env.action_space.shape or env.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 = SubprocVectorEnv([lambda: make_atari_env(args)
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for _ in range(args.training_num)])
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test_envs = SubprocVectorEnv([lambda: make_atari_env_watch(args)
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for _ in range(args.test_num)])
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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 = C51(*args.state_shape, args.action_shape,
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args.num_atoms, args.device)
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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# define policy
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policy = C51Policy(
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net, optim, args.gamma, args.num_atoms, args.v_min, args.v_max,
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args.n_step, 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(
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args.resume_path, map_location=args.device
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))
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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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buffer = ReplayBuffer(args.buffer_size, ignore_obs_next=True,
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save_only_last_obs=True, stack_num=args.frames_stack)
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# collector
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train_collector = Collector(policy, train_envs, buffer)
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test_collector = Collector(policy, test_envs)
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# log
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log_path = os.path.join(args.logdir, args.task, 'c51')
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writer = SummaryWriter(log_path)
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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.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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writer.add_scalar('train/eps', eps, global_step=env_step)
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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("Testing agent ...")
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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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test_collector.reset()
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result = test_collector.collect(n_episode=[1] * args.test_num,
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render=args.render)
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pprint.pprint(result)
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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 * 4)
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# trainer
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result = offpolicy_trainer(
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policy, train_collector, test_collector, args.epoch,
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args.step_per_epoch, args.collect_per_step, args.test_num,
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args.batch_size, train_fn=train_fn, test_fn=test_fn,
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stop_fn=stop_fn, save_fn=save_fn, writer=writer, test_in_train=False)
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pprint.pprint(result)
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watch()
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if __name__ == '__main__':
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test_c51(get_args())
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