522f7fbf98
This PR adds strict typing to the output of `update` and `learn` in all
policies. This will likely be the last large refactoring PR before the
next release (0.6.0, not 1.0.0), so it requires some attention. Several
difficulties were encountered on the path to that goal:
1. The policy hierarchy is actually "broken" in the sense that the keys
of dicts that were output by `learn` did not follow the same enhancement
(inheritance) pattern as the policies. This is a real problem and should
be addressed in the near future. Generally, several aspects of the
policy design and hierarchy might deserve a dedicated discussion.
2. Each policy needs to be generic in the stats return type, because one
might want to extend it at some point and then also extend the stats.
Even within the source code base this pattern is necessary in many
places.
3. The interaction between learn and update is a bit quirky, we
currently handle it by having update modify special field inside
TrainingStats, whereas all other fields are handled by learn.
4. The IQM module is a policy wrapper and required a
TrainingStatsWrapper. The latter relies on a bunch of black magic.
They were addressed by:
1. Live with the broken hierarchy, which is now made visible by bounds
in generics. We use type: ignore where appropriate.
2. Make all policies generic with bounds following the policy
inheritance hierarchy (which is incorrect, see above). We experimented a
bit with nested TrainingStats classes, but that seemed to add more
complexity and be harder to understand. Unfortunately, mypy thinks that
the code below is wrong, wherefore we have to add `type: ignore` to the
return of each `learn`
```python
T = TypeVar("T", bound=int)
def f() -> T:
return 3
```
3. See above
4. Write representative tests for the `TrainingStatsWrapper`. Still, the
black magic might cause nasty surprises down the line (I am not proud of
it)...
Closes #933
---------
Co-authored-by: Maximilian Huettenrauch <m.huettenrauch@appliedai.de>
Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de>
264 lines
9.4 KiB
Python
264 lines
9.4 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 atari_network import DQN
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from atari_wrapper import make_atari_env
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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 DQNPolicy
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from tianshou.policy.modelbased.icm import ICMPolicy
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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.discrete import 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="PongNoFrameskip-v4")
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--scale-obs", type=int, default=0)
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parser.add_argument("--eps-test", type=float, default=0.005)
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parser.add_argument("--eps-train", type=float, default=1.0)
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parser.add_argument("--eps-train-final", type=float, default=0.05)
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parser.add_argument("--buffer-size", type=int, default=100000)
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parser.add_argument("--lr", type=float, default=0.0001)
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parser.add_argument("--gamma", type=float, default=0.99)
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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=500)
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parser.add_argument("--epoch", type=int, default=100)
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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=10)
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parser.add_argument("--update-per-step", type=float, default=0.1)
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parser.add_argument("--batch-size", type=int, default=32)
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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=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("--frames-stack", 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="atari.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("--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_dqn(args=get_args()):
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env, train_envs, test_envs = make_atari_env(
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args.task,
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args.seed,
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args.training_num,
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args.test_num,
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scale=args.scale_obs,
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frame_stack=args.frames_stack,
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)
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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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# 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(*args.state_shape, args.action_shape, args.device).to(args.device)
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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# define policy
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policy = DQNPolicy(
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model=net,
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optim=optim,
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action_space=env.action_space,
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discount_factor=args.gamma,
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estimation_step=args.n_step,
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target_update_freq=args.target_update_freq,
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)
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if args.icm_lr_scale > 0:
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feature_net = DQN(*args.state_shape, args.action_shape, args.device, features_only=True)
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action_dim = np.prod(args.action_shape)
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feature_dim = feature_net.output_dim
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icm_net = IntrinsicCuriosityModule(
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feature_net.net,
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feature_dim,
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action_dim,
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hidden_sizes=[512],
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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=policy,
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model=icm_net,
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optim=icm_optim,
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action_space=env.action_space,
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lr_scale=args.icm_lr_scale,
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reward_scale=args.icm_reward_scale,
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forward_loss_weight=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 = "dqn_icm" if args.icm_lr_scale > 0 else "dqn"
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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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if "Pong" in args.task:
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return mean_rewards >= 20
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return False
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def train_fn(epoch, env_step):
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# nature DQN setting, linear decay in the first 1M steps
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if env_step <= 1e6:
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eps = args.eps_train - env_step / 1e6 * (args.eps_train - args.eps_train_final)
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else:
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eps = args.eps_train_final
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policy.set_eps(eps)
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if env_step % 1000 == 0:
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logger.write("train/env_step", env_step, {"train/eps": eps})
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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, f"checkpoint_{epoch}.pth")
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torch.save({"model": policy.state_dict()}, ckpt_path)
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return ckpt_path
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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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policy.set_eps(args.eps_test)
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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.returns_stat.mean
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print(f"Mean reward (over {result['n/ep']} episodes): {rew}")
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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 = 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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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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update_per_step=args.update_per_step,
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test_in_train=False,
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resume_from_log=args.resume_id is not None,
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save_checkpoint_fn=save_checkpoint_fn,
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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_dqn(get_args())
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