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>
236 lines
8.8 KiB
Python
236 lines
8.8 KiB
Python
import argparse
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import os
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import pickle
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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 tianshou.data import Collector, PrioritizedVectorReplayBuffer, VectorReplayBuffer
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from tianshou.env import DummyVectorEnv
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from tianshou.policy import RainbowPolicy
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from tianshou.trainer import OffpolicyTrainer
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from tianshou.utils import TensorboardLogger
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from tianshou.utils.net.common import Net
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from tianshou.utils.net.discrete import NoisyLinear
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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="CartPole-v0")
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parser.add_argument("--reward-threshold", type=float, default=None)
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parser.add_argument("--seed", type=int, default=1626)
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parser.add_argument("--eps-test", type=float, default=0.05)
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parser.add_argument("--eps-train", type=float, default=0.1)
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parser.add_argument("--buffer-size", type=int, default=20000)
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parser.add_argument("--lr", type=float, default=1e-3)
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parser.add_argument("--gamma", type=float, default=0.9)
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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=-10.0)
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parser.add_argument("--v-max", type=float, default=10.0)
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parser.add_argument("--noisy-std", type=float, default=0.1)
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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=320)
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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=8000)
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parser.add_argument("--step-per-collect", type=int, default=8)
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parser.add_argument("--update-per-step", type=float, default=0.125)
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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=[128, 128, 128, 128])
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parser.add_argument("--training-num", type=int, default=8)
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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=0.0)
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parser.add_argument("--prioritized-replay", action="store_true", default=False)
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parser.add_argument("--alpha", type=float, default=0.6)
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parser.add_argument("--beta", type=float, default=0.4)
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parser.add_argument("--beta-final", type=float, default=1.0)
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parser.add_argument("--resume", action="store_true")
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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("--save-interval", type=int, default=4)
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return parser.parse_known_args()[0]
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def test_rainbow(args=get_args()):
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env = gym.make(args.task)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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if args.reward_threshold is None:
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default_reward_threshold = {"CartPole-v0": 195}
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args.reward_threshold = default_reward_threshold.get(args.task, env.spec.reward_threshold)
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# train_envs = gym.make(args.task)
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# you can also use tianshou.env.SubprocVectorEnv
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train_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.training_num)])
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# test_envs = gym.make(args.task)
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test_envs = DummyVectorEnv([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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train_envs.seed(args.seed)
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test_envs.seed(args.seed)
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# model
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def noisy_linear(x, y):
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return NoisyLinear(x, y, args.noisy_std)
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net = Net(
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args.state_shape,
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args.action_shape,
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hidden_sizes=args.hidden_sizes,
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device=args.device,
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softmax=True,
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num_atoms=args.num_atoms,
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dueling_param=({"linear_layer": noisy_linear}, {"linear_layer": noisy_linear}),
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)
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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policy = RainbowPolicy(
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model=net,
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optim=optim,
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discount_factor=args.gamma,
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action_space=env.action_space,
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num_atoms=args.num_atoms,
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v_min=args.v_min,
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v_max=args.v_max,
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estimation_step=args.n_step,
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target_update_freq=args.target_update_freq,
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).to(args.device)
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# buffer
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if args.prioritized_replay:
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buf = PrioritizedVectorReplayBuffer(
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args.buffer_size,
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buffer_num=len(train_envs),
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alpha=args.alpha,
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beta=args.beta,
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weight_norm=True,
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)
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else:
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buf = VectorReplayBuffer(args.buffer_size, buffer_num=len(train_envs))
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# collector
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train_collector = Collector(policy, train_envs, buf, exploration_noise=True)
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test_collector = Collector(policy, test_envs, exploration_noise=True)
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# policy.set_eps(1)
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train_collector.collect(n_step=args.batch_size * args.training_num)
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# log
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log_path = os.path.join(args.logdir, args.task, "rainbow")
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writer = SummaryWriter(log_path)
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logger = TensorboardLogger(writer, save_interval=args.save_interval)
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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):
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return mean_rewards >= args.reward_threshold
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def train_fn(epoch, env_step):
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# eps annealing, just a demo
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if env_step <= 10000:
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policy.set_eps(args.eps_train)
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elif env_step <= 50000:
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eps = args.eps_train - (env_step - 10000) / 40000 * (0.9 * args.eps_train)
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policy.set_eps(eps)
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else:
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policy.set_eps(0.1 * args.eps_train)
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# beta annealing, just a demo
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if args.prioritized_replay:
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if env_step <= 10000:
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beta = args.beta
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elif env_step <= 50000:
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beta = args.beta - (env_step - 10000) / 40000 * (args.beta - args.beta_final)
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else:
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beta = args.beta_final
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buf.set_beta(beta)
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def test_fn(epoch, env_step):
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policy.set_eps(args.eps_test)
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def save_checkpoint_fn(epoch, env_step, gradient_step):
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# see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
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ckpt_path = os.path.join(log_path, "checkpoint.pth")
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# Example: saving by epoch num
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# ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
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torch.save(
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{
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"model": policy.state_dict(),
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"optim": optim.state_dict(),
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},
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ckpt_path,
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)
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buffer_path = os.path.join(log_path, "train_buffer.pkl")
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with open(buffer_path, "wb") as f:
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pickle.dump(train_collector.buffer, f)
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return ckpt_path
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if args.resume:
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# load from existing checkpoint
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print(f"Loading agent under {log_path}")
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ckpt_path = os.path.join(log_path, "checkpoint.pth")
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if os.path.exists(ckpt_path):
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checkpoint = torch.load(ckpt_path, map_location=args.device)
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policy.load_state_dict(checkpoint["model"])
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policy.optim.load_state_dict(checkpoint["optim"])
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print("Successfully restore policy and optim.")
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else:
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print("Fail to restore policy and optim.")
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buffer_path = os.path.join(log_path, "train_buffer.pkl")
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if os.path.exists(buffer_path):
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with open(buffer_path, "rb") as f:
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train_collector.buffer = pickle.load(f)
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print("Successfully restore buffer.")
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else:
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print("Fail to restore buffer.")
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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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update_per_step=args.update_per_step,
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train_fn=train_fn,
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test_fn=test_fn,
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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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resume_from_log=args.resume,
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save_checkpoint_fn=save_checkpoint_fn,
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).run()
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assert stop_fn(result["best_reward"])
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if __name__ == "__main__":
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pprint.pprint(result)
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# Let's watch its performance!
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env = gym.make(args.task)
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policy.eval()
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policy.set_eps(args.eps_test)
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collector = Collector(policy, env)
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result = collector.collect(n_episode=1, render=args.render)
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rews, lens = result["rews"], result["lens"]
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print(f"Final reward: {rews.mean()}, length: {lens.mean()}")
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def test_rainbow_resume(args=get_args()):
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args.resume = True
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test_rainbow(args)
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def test_prainbow(args=get_args()):
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args.prioritized_replay = True
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args.gamma = 0.95
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args.seed = 1
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test_rainbow(args)
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if __name__ == "__main__":
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test_rainbow(get_args())
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