226 lines
7.9 KiB
Python
226 lines
7.9 KiB
Python
import argparse
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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 env import Env
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from network import DQN
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.data import Collector, VectorReplayBuffer
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from tianshou.env import ShmemVectorEnv
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from tianshou.policy import A2CPolicy, ICMPolicy
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from tianshou.trainer import onpolicy_trainer
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from tianshou.utils import TensorboardLogger
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from tianshou.utils.net.common import ActorCritic
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from tianshou.utils.net.discrete import Actor, Critic, IntrinsicCuriosityModule
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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='D2_navigation')
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parser.add_argument('--seed', type=int, default=0)
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parser.add_argument('--buffer-size', type=int, default=2000000)
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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('--epoch', type=int, default=300)
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parser.add_argument('--step-per-epoch', type=int, default=100000)
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parser.add_argument('--episode-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('--update-per-step', type=int, default=1)
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parser.add_argument('--batch-size', type=int, default=64)
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parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[512])
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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=100)
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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('--skip-num', 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(
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'--save-lmp',
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default=False,
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action='store_true',
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help='save lmp file for replay whole episode'
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)
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parser.add_argument('--save-buffer-name', type=str, default=None)
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parser.add_argument(
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'--icm-lr-scale',
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type=float,
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default=0.,
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help='use intrinsic curiosity module with this lr scale'
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)
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parser.add_argument(
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'--icm-reward-scale',
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type=float,
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default=0.01,
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help='scaling factor for intrinsic curiosity reward'
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)
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parser.add_argument(
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'--icm-forward-loss-weight',
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type=float,
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default=0.2,
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help='weight for the forward model loss in ICM'
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)
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return parser.parse_args()
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def test_a2c(args=get_args()):
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args.cfg_path = f"maps/{args.task}.cfg"
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args.wad_path = f"maps/{args.task}.wad"
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args.res = (args.skip_num, 84, 84)
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env = Env(args.cfg_path, args.frames_stack, args.res)
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args.state_shape = args.res
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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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[
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lambda: Env(args.cfg_path, args.frames_stack, args.res)
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for _ in range(args.training_num)
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]
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)
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test_envs = ShmemVectorEnv(
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[
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lambda: Env(args.cfg_path, args.frames_stack, args.res, args.save_lmp)
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for _ in range(min(os.cpu_count() - 1, args.test_num))
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]
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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 = DQN(
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*args.state_shape, args.action_shape, device=args.device, features_only=True
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)
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actor = Actor(
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net, args.action_shape, hidden_sizes=args.hidden_sizes, device=args.device
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)
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critic = Critic(net, hidden_sizes=args.hidden_sizes, device=args.device)
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optim = torch.optim.Adam(ActorCritic(actor, critic).parameters(), lr=args.lr)
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# define policy
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dist = torch.distributions.Categorical
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policy = A2CPolicy(actor, critic, optim, dist).to(args.device)
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if args.icm_lr_scale > 0:
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feature_net = DQN(
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*args.state_shape,
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args.action_shape,
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device=args.device,
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features_only=True
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)
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action_dim = np.prod(args.action_shape)
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feature_dim = feature_net.output_dim
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icm_net = IntrinsicCuriosityModule(
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feature_net.net,
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feature_dim,
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action_dim,
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hidden_sizes=args.hidden_sizes,
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device=args.device
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)
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icm_optim = torch.optim.adam(icm_net.parameters(), lr=args.lr)
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policy = ICMPolicy(
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policy, icm_net, icm_optim, args.icm_lr_scale, args.icm_reward_scale,
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args.icm_forward_loss_weight
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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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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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# 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(args.logdir, args.task, 'a2c')
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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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return False
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def watch():
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# watch agent's performance
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print("Setup test envs ...")
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policy.eval()
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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 = VectorReplayBuffer(
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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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)
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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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lens = result["lens"].mean() * args.skip_num
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print(f'Mean reward (over {result["n/ep"]} episodes): {rew}')
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print(f'Mean length (over {result["n/ep"]} episodes): {lens}')
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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 = onpolicy_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.repeat_per_collect,
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args.test_num,
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args.batch_size,
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episode_per_collect=args.episode_per_collect,
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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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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_a2c(get_args())
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