600f4bbd55
Preparation for #914 and #920 Changes formatting to ruff and black. Remove python 3.8 ## Additional Changes - Removed flake8 dependencies - Adjusted pre-commit. Now CI and Make use pre-commit, reducing the duplication of linting calls - Removed check-docstyle option (ruff is doing that) - Merged format and lint. In CI the format-lint step fails if any changes are done, so it fulfills the lint functionality. --------- Co-authored-by: Jiayi Weng <jiayi@openai.com>
291 lines
10 KiB
Python
291 lines
10 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 sys
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import numpy as np
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import torch
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from env import make_vizdoom_env
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from network import DQN
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from torch.optim.lr_scheduler import LambdaLR
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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.policy import ICMPolicy, PPOPolicy
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from tianshou.trainer import OnpolicyTrainer
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from tianshou.utils import TensorboardLogger, WandbLogger
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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="D1_basic")
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--buffer-size", type=int, default=100000)
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parser.add_argument("--lr", type=float, default=0.00002)
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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("--step-per-collect", type=int, default=1000)
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parser.add_argument("--repeat-per-collect", type=int, default=4)
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parser.add_argument("--batch-size", type=int, default=256)
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parser.add_argument("--hidden-size", type=int, 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("--rew-norm", type=int, default=False)
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parser.add_argument("--vf-coef", type=float, default=0.5)
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parser.add_argument("--ent-coef", type=float, default=0.01)
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parser.add_argument("--gae-lambda", type=float, default=0.95)
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parser.add_argument("--lr-decay", type=int, default=True)
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parser.add_argument("--max-grad-norm", type=float, default=0.5)
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parser.add_argument("--eps-clip", type=float, default=0.2)
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parser.add_argument("--dual-clip", type=float, default=None)
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parser.add_argument("--value-clip", type=int, default=0)
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parser.add_argument("--norm-adv", type=int, default=1)
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parser.add_argument("--recompute-adv", type=int, default=0)
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parser.add_argument("--logdir", type=str, default="log")
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parser.add_argument("--render", type=float, default=0.0)
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parser.add_argument(
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"--device",
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type=str,
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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("--resume-id", type=str, default=None)
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parser.add_argument(
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"--logger",
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type=str,
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default="tensorboard",
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choices=["tensorboard", "wandb"],
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)
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parser.add_argument("--wandb-project", type=str, default="vizdoom.benchmark")
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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=0.0,
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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_ppo(args=get_args()):
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# make environments
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env, train_envs, test_envs = make_vizdoom_env(
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args.task,
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args.skip_num,
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(args.frames_stack, 84, 84),
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args.save_lmp,
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args.seed,
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args.training_num,
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args.test_num,
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)
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args.state_shape = env.observation_space.shape
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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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# seed
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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# define model
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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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output_dim=args.hidden_size,
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)
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actor = Actor(net, args.action_shape, device=args.device, softmax_output=False)
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critic = Critic(net, device=args.device)
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optim = torch.optim.Adam(ActorCritic(actor, critic).parameters(), lr=args.lr)
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lr_scheduler = None
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if args.lr_decay:
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# decay learning rate to 0 linearly
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max_update_num = np.ceil(args.step_per_epoch / args.step_per_collect) * args.epoch
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lr_scheduler = LambdaLR(optim, lr_lambda=lambda epoch: 1 - epoch / max_update_num)
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# define policy
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def dist(p):
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return torch.distributions.Categorical(logits=p)
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policy = PPOPolicy(
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actor,
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critic,
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optim,
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dist,
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discount_factor=args.gamma,
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gae_lambda=args.gae_lambda,
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max_grad_norm=args.max_grad_norm,
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vf_coef=args.vf_coef,
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ent_coef=args.ent_coef,
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reward_normalization=args.rew_norm,
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action_scaling=False,
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lr_scheduler=lr_scheduler,
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action_space=env.action_space,
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eps_clip=args.eps_clip,
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value_clip=args.value_clip,
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dual_clip=args.dual_clip,
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advantage_normalization=args.norm_adv,
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recompute_advantage=args.recompute_adv,
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).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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output_dim=args.hidden_size,
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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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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,
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icm_net,
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icm_optim,
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args.icm_lr_scale,
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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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now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
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args.algo_name = "ppo_icm" if args.icm_lr_scale > 0 else "ppo"
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log_name = os.path.join(args.task, args.algo_name, str(args.seed), now)
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log_path = os.path.join(args.logdir, log_name)
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# logger
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if args.logger == "wandb":
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logger = WandbLogger(
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save_interval=1,
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name=log_name.replace(os.path.sep, "__"),
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run_id=args.resume_id,
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config=args,
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project=args.wandb_project,
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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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if args.logger == "tensorboard":
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logger = TensorboardLogger(writer)
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else: # wandb
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logger.load(writer)
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def save_best_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: float) -> bool:
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if env.spec.reward_threshold:
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return mean_rewards >= env.spec.reward_threshold
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return False
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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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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(n_episode=args.test_num, render=args.render)
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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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sys.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 = OnpolicyTrainer(
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policy=policy,
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train_collector=train_collector,
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test_collector=test_collector,
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max_epoch=args.epoch,
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step_per_epoch=args.step_per_epoch,
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repeat_per_collect=args.repeat_per_collect,
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episode_per_test=args.test_num,
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batch_size=args.batch_size,
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step_per_collect=args.step_per_collect,
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stop_fn=stop_fn,
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save_best_fn=save_best_fn,
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logger=logger,
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test_in_train=False,
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).run()
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pprint.pprint(result)
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watch()
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if __name__ == "__main__":
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test_ppo(get_args())
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