b900fdf6f2
Closes #947 This removes all kwargs from all policy constructors. While doing that, I also improved several names and added a whole lot of TODOs. ## Functional changes: 1. Added possibility to pass None as `critic2` and `critic2_optim`. In fact, the default behavior then should cover the absolute majority of cases 2. Added a function called `clone_optimizer` as a temporary measure to support passing `critic2_optim=None` ## Breaking changes: 1. `action_space` is no longer optional. In fact, it already was non-optional, as there was a ValueError in BasePolicy.init. So now several examples were fixed to reflect that 2. `reward_normalization` removed from DDPG and children. It was never allowed to pass it as `True` there, an error would have been raised in `compute_n_step_reward`. Now I removed it from the interface 3. renamed `critic1` and similar to `critic`, in order to have uniform interfaces. Note that the `critic` in DDPG was optional for the sole reason that child classes used `critic1`. I removed this optionality (DDPG can't do anything with `critic=None`) 4. Several renamings of fields (mostly private to public, so backwards compatible) ## Additional changes: 1. Removed type and default declaration from docstring. This kind of duplication is really not necessary 2. Policy constructors are now only called using named arguments, not a fragile mixture of positional and named as before 5. Minor beautifications in typing and code 6. Generally shortened docstrings and made them uniform across all policies (hopefully) ## Comment: With these changes, several problems in tianshou's inheritance hierarchy become more apparent. I tried highlighting them for future work. --------- Co-authored-by: Dominik Jain <d.jain@appliedai.de>
233 lines
8.3 KiB
Python
233 lines
8.3 KiB
Python
#!/usr/bin/env python3
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# isort: skip_file
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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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import wandb
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.data import (
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Collector,
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HERReplayBuffer,
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HERVectorReplayBuffer,
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ReplayBuffer,
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VectorReplayBuffer,
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)
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from tianshou.env import ShmemVectorEnv, TruncatedAsTerminated
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from tianshou.exploration import GaussianNoise
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from tianshou.policy import DDPGPolicy
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from tianshou.trainer import OffpolicyTrainer
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from tianshou.utils import TensorboardLogger, WandbLogger
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from tianshou.utils.net.common import Net, get_dict_state_decorator
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from tianshou.utils.net.continuous import Actor, Critic
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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="FetchReach-v3")
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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("--hidden-sizes", type=int, nargs="*", default=[256, 256])
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parser.add_argument("--actor-lr", type=float, default=1e-3)
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parser.add_argument("--critic-lr", type=float, default=3e-3)
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parser.add_argument("--gamma", type=float, default=0.99)
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parser.add_argument("--tau", type=float, default=0.005)
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parser.add_argument("--exploration-noise", type=float, default=0.1)
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parser.add_argument("--start-timesteps", type=int, default=25000)
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parser.add_argument("--epoch", type=int, default=10)
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parser.add_argument("--step-per-epoch", type=int, default=5000)
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parser.add_argument("--step-per-collect", type=int, default=1)
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parser.add_argument("--update-per-step", type=int, default=1)
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parser.add_argument("--n-step", type=int, default=1)
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parser.add_argument("--batch-size", type=int, default=512)
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parser.add_argument("--replay-buffer", type=str, default="her", choices=["normal", "her"])
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parser.add_argument("--her-horizon", type=int, default=50)
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parser.add_argument("--her-future-k", type=int, default=8)
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parser.add_argument("--training-num", type=int, default=1)
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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=0.0)
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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("--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="HER-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 make_fetch_env(task, training_num, test_num):
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env = TruncatedAsTerminated(gym.make(task))
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train_envs = ShmemVectorEnv(
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[lambda: TruncatedAsTerminated(gym.make(task)) for _ in range(training_num)],
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)
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test_envs = ShmemVectorEnv(
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[lambda: TruncatedAsTerminated(gym.make(task)) for _ in range(test_num)],
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)
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return env, train_envs, test_envs
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def test_ddpg(args=get_args()):
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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 = "ddpg"
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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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logger.wandb_run.config.setdefaults(vars(args))
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args = argparse.Namespace(**wandb.config)
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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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env, train_envs, test_envs = make_fetch_env(args.task, args.training_num, args.test_num)
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args.state_shape = {
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"observation": env.observation_space["observation"].shape,
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"achieved_goal": env.observation_space["achieved_goal"].shape,
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"desired_goal": env.observation_space["desired_goal"].shape,
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}
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args.action_shape = env.action_space.shape or env.action_space.n
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args.max_action = env.action_space.high[0]
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args.exploration_noise = args.exploration_noise * args.max_action
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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:", np.min(env.action_space.low), np.max(env.action_space.high))
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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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# model
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dict_state_dec, flat_state_shape = get_dict_state_decorator(
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state_shape=args.state_shape,
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keys=["observation", "achieved_goal", "desired_goal"],
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)
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net_a = dict_state_dec(Net)(
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flat_state_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 = dict_state_dec(Actor)(
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net_a,
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args.action_shape,
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max_action=args.max_action,
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device=args.device,
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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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net_c = dict_state_dec(Net)(
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flat_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 = dict_state_dec(Critic)(net_c, 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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policy = DDPGPolicy(
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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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tau=args.tau,
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gamma=args.gamma,
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exploration_noise=GaussianNoise(sigma=args.exploration_noise),
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estimation_step=args.n_step,
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action_space=env.action_space,
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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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def compute_reward_fn(ag: np.ndarray, g: np.ndarray):
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return env.compute_reward(ag, g, {})
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if args.replay_buffer == "normal":
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if args.training_num > 1:
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buffer = VectorReplayBuffer(args.buffer_size, len(train_envs))
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else:
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buffer = ReplayBuffer(args.buffer_size)
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else:
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if args.training_num > 1:
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buffer = HERVectorReplayBuffer(
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args.buffer_size,
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len(train_envs),
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compute_reward_fn=compute_reward_fn,
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horizon=args.her_horizon,
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future_k=args.her_future_k,
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)
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else:
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buffer = HERReplayBuffer(
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args.buffer_size,
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compute_reward_fn=compute_reward_fn,
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horizon=args.her_horizon,
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future_k=args.her_future_k,
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)
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train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
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test_collector = Collector(policy, test_envs)
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train_collector.collect(n_step=args.start_timesteps, random=True)
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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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if not args.watch:
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# trainer
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result = OffpolicyTrainer(
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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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step_per_collect=args.step_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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save_best_fn=save_best_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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).run()
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pprint.pprint(result)
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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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result = test_collector.collect(n_episode=args.test_num, render=args.render)
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print(f'Final reward: {result["rews"].mean()}, length: {result["lens"].mean()}')
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if __name__ == "__main__":
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test_ddpg()
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