8a0629ded6
Closes #952 - `SamplingConfig` supports `batch_size=None`. #1077 - tests and examples are covered by `mypy`. #1077 - `NetBase` is more used, stricter typing by making it generic. #1077 - `utils.net.common.Recurrent` now receives and returns a `RecurrentStateBatch` instead of a dict. #1077 --------- Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de>
379 lines
11 KiB
Python
379 lines
11 KiB
Python
#!/usr/bin/env python3
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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 gymnasium as gym
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import numpy as np
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import torch
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from torch.utils.tensorboard import SummaryWriter
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from examples.offline.utils import load_buffer_d4rl
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from tianshou.data import Collector
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from tianshou.env import SubprocVectorEnv
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from tianshou.policy import CQLPolicy
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from tianshou.policy.base import BasePolicy
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from tianshou.trainer import OfflineTrainer
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from tianshou.utils import TensorboardLogger, WandbLogger
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from tianshou.utils.net.common import Net
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from tianshou.utils.net.continuous import ActorProb, Critic
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from tianshou.utils.space_info import SpaceInfo
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def get_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--task",
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type=str,
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default="Hopper-v2",
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help="The name of the OpenAI Gym environment to train on.",
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)
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parser.add_argument(
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"--seed",
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type=int,
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default=0,
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help="The random seed to use.",
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)
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parser.add_argument(
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"--expert-data-task",
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type=str,
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default="hopper-expert-v2",
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help="The name of the OpenAI Gym environment to use for expert data collection.",
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)
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parser.add_argument(
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"--buffer-size",
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type=int,
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default=1000000,
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help="The size of the replay buffer.",
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)
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parser.add_argument(
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"--hidden-sizes",
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type=int,
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nargs="*",
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default=[256, 256],
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help="The list of hidden sizes for the neural networks.",
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)
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parser.add_argument(
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"--actor-lr",
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type=float,
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default=1e-4,
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help="The learning rate for the actor network.",
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)
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parser.add_argument(
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"--critic-lr",
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type=float,
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default=3e-4,
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help="The learning rate for the critic network.",
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)
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parser.add_argument(
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"--alpha",
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type=float,
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default=0.2,
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help="The weight of the entropy term in the loss function.",
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)
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parser.add_argument(
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"--auto-alpha",
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default=True,
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action="store_true",
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help="Whether to use automatic entropy tuning.",
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)
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parser.add_argument(
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"--alpha-lr",
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type=float,
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default=1e-4,
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help="The learning rate for the entropy tuning.",
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)
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parser.add_argument(
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"--cql-alpha-lr",
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type=float,
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default=3e-4,
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help="The learning rate for the CQL entropy tuning.",
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)
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parser.add_argument(
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"--start-timesteps",
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type=int,
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default=10000,
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help="The number of timesteps before starting to train.",
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)
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parser.add_argument(
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"--epoch",
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type=int,
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default=200,
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help="The number of epochs to train for.",
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)
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parser.add_argument(
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"--step-per-epoch",
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type=int,
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default=5000,
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help="The number of steps per epoch.",
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)
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parser.add_argument(
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"--n-step",
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type=int,
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default=3,
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help="The number of steps to use for N-step TD learning.",
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)
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parser.add_argument(
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"--batch-size",
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type=int,
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default=256,
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help="The batch size for training.",
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)
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parser.add_argument(
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"--tau",
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type=float,
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default=0.005,
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help="The soft target update coefficient.",
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)
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parser.add_argument(
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"--temperature",
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type=float,
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default=1.0,
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help="The temperature for the Boltzmann policy.",
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)
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parser.add_argument(
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"--cql-weight",
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type=float,
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default=1.0,
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help="The weight of the CQL loss term.",
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)
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parser.add_argument(
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"--with-lagrange",
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type=bool,
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default=True,
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help="Whether to use the Lagrange multiplier for CQL.",
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)
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parser.add_argument(
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"--calibrated",
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type=bool,
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default=True,
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help="Whether to use calibration for CQL.",
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)
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parser.add_argument(
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"--lagrange-threshold",
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type=float,
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default=10.0,
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help="The Lagrange multiplier threshold for CQL.",
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)
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parser.add_argument("--gamma", type=float, default=0.99, help="The discount factor")
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parser.add_argument(
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"--eval-freq",
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type=int,
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default=1,
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help="The frequency of evaluation.",
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)
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parser.add_argument(
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"--test-num",
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type=int,
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default=10,
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help="The number of episodes to evaluate for.",
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)
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parser.add_argument(
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"--logdir",
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type=str,
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default="log",
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help="The directory to save logs to.",
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)
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parser.add_argument(
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"--render",
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type=float,
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default=1 / 35,
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help="The frequency of rendering the environment.",
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)
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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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help="The device to train on (cpu or cuda).",
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)
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parser.add_argument(
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"--resume-path",
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type=str,
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default=None,
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help="The path to the checkpoint to resume from.",
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)
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parser.add_argument(
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"--resume-id",
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type=str,
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default=None,
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help="The ID of the checkpoint to resume from.",
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)
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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="offline_d4rl.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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return parser.parse_args()
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def test_cql() -> None:
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args = get_args()
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env = gym.make(args.task)
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assert isinstance(env.action_space, gym.spaces.Box)
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space_info = SpaceInfo.from_env(env)
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args.state_shape = space_info.observation_info.obs_shape
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args.action_shape = space_info.action_info.action_shape
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args.max_action = space_info.action_info.max_action
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args.min_action = space_info.action_info.min_action
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print("device:", args.device)
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print("Observations shape:", args.state_shape)
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print("Actions shape:", args.action_shape)
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print("Action range:", args.min_action, args.max_action)
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args.state_dim = space_info.observation_info.obs_dim
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args.action_dim = space_info.action_info.action_dim
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print("Max_action", args.max_action)
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# test_envs = gym.make(args.task)
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test_envs = SubprocVectorEnv([lambda: gym.make(args.task) 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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test_envs.seed(args.seed)
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# model
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# actor network
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net_a = Net(
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state_shape=args.state_shape,
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action_shape=args.action_shape,
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hidden_sizes=args.hidden_sizes,
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device=args.device,
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)
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actor = ActorProb(
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net_a,
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action_shape=args.action_shape,
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device=args.device,
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unbounded=True,
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conditioned_sigma=True,
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).to(args.device)
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actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
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# critic network
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net_c1 = Net(
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state_shape=args.state_shape,
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action_shape=args.action_shape,
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hidden_sizes=args.hidden_sizes,
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concat=True,
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device=args.device,
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)
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net_c2 = Net(
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state_shape=args.state_shape,
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action_shape=args.action_shape,
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hidden_sizes=args.hidden_sizes,
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concat=True,
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device=args.device,
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)
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critic = Critic(net_c1, device=args.device).to(args.device)
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critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr)
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critic2 = Critic(net_c2, device=args.device).to(args.device)
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critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
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if args.auto_alpha:
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target_entropy = -args.action_dim
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log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
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alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
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args.alpha = (target_entropy, log_alpha, alpha_optim)
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policy: CQLPolicy = CQLPolicy(
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actor=actor,
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actor_optim=actor_optim,
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critic=critic,
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critic_optim=critic_optim,
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action_space=env.action_space,
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critic2=critic2,
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critic2_optim=critic2_optim,
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calibrated=args.calibrated,
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cql_alpha_lr=args.cql_alpha_lr,
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cql_weight=args.cql_weight,
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tau=args.tau,
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gamma=args.gamma,
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alpha=args.alpha,
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temperature=args.temperature,
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with_lagrange=args.with_lagrange,
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lagrange_threshold=args.lagrange_threshold,
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min_action=args.min_action,
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max_action=args.max_action,
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device=args.device,
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)
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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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# collector
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test_collector = Collector(policy, test_envs)
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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 = "cql"
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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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writer = SummaryWriter(log_path)
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writer.add_text("args", str(args))
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logger: WandbLogger | TensorboardLogger
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if args.logger == "tensorboard":
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logger = TensorboardLogger(writer)
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else:
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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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logger.load(writer)
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def save_best_fn(policy: BasePolicy) -> None:
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torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))
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def watch() -> None:
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if args.resume_path is None:
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args.resume_path = os.path.join(log_path, "policy.pth")
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policy.load_state_dict(torch.load(args.resume_path, map_location=torch.device("cpu")))
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policy.eval()
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collector = Collector(policy, env)
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collector.collect(n_episode=1, render=1 / 35)
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if not args.watch:
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replay_buffer = load_buffer_d4rl(args.expert_data_task)
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# trainer
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result = OfflineTrainer(
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policy=policy,
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buffer=replay_buffer,
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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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episode_per_test=args.test_num,
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batch_size=args.batch_size,
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save_best_fn=save_best_fn,
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logger=logger,
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).run()
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pprint.pprint(result)
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else:
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watch()
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# Let's watch its performance!
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policy.eval()
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test_envs.seed(args.seed)
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test_collector.reset()
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collector_stats = test_collector.collect(n_episode=args.test_num, render=args.render)
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print(collector_stats)
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if __name__ == "__main__":
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test_cql()
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