forked from pytorch/rl
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[Algorithm] Online Decision transformer (pytorch#1149)
Co-authored-by: vmoens <vincentmoens@gmail.com> Co-authored-by: Mateusz Guzek <matguzek@meta.com>
- Loading branch information
3 people
authored
Aug 30, 2023
1 parent
121ecd9
commit b444007
Showing
32 changed files
with
2,537 additions
and
73 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -28,3 +28,4 @@ dependencies: | |
| - av | ||
| - coverage | ||
| - ray | ||
| - transformers | ||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,98 @@ | ||
| # Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| # | ||
| # This source code is licensed under the MIT license found in the | ||
| # LICENSE file in the root directory of this source tree. | ||
| """Decision Transformer Example. | ||
| This is a self-contained example of an offline Decision Transformer training script. | ||
| The helper functions are coded in the utils.py associated with this script. | ||
| """ | ||
|
|
||
| import hydra | ||
| import torch | ||
| import tqdm | ||
|
|
||
| from torchrl.envs.utils import ExplorationType, set_exploration_type | ||
| from torchrl.modules.tensordict_module import DecisionTransformerInferenceWrapper | ||
|
|
||
| from utils import ( | ||
| make_dt_loss, | ||
| make_dt_model, | ||
| make_dt_optimizer, | ||
| make_env, | ||
| make_logger, | ||
| make_offline_replay_buffer, | ||
| ) | ||
|
|
||
|
|
||
| @hydra.main(config_path=".", config_name="dt_config") | ||
| def main(cfg: "DictConfig"): # noqa: F821 | ||
| model_device = cfg.optim.device | ||
| logger = make_logger(cfg) | ||
| offline_buffer, obs_loc, obs_std = make_offline_replay_buffer( | ||
| cfg.replay_buffer, cfg.env.reward_scaling | ||
| ) | ||
| test_env = make_env(cfg.env, obs_loc, obs_std) | ||
| actor = make_dt_model(cfg) | ||
| policy = actor.to(model_device) | ||
|
|
||
| loss_module = make_dt_loss(cfg.loss, actor) | ||
| transformer_optim, scheduler = make_dt_optimizer(cfg.optim, loss_module) | ||
| inference_policy = DecisionTransformerInferenceWrapper( | ||
| policy=policy, | ||
| inference_context=cfg.env.inference_context, | ||
| ).to(model_device) | ||
|
|
||
| pbar = tqdm.tqdm(total=cfg.optim.pretrain_gradient_steps) | ||
|
|
||
| r0 = None | ||
| l0 = None | ||
|
|
||
| pretrain_gradient_steps = cfg.optim.pretrain_gradient_steps | ||
| clip_grad = cfg.optim.clip_grad | ||
| eval_steps = cfg.logger.eval_steps | ||
| pretrain_log_interval = cfg.logger.pretrain_log_interval | ||
| reward_scaling = cfg.env.reward_scaling | ||
|
|
||
| print(" ***Pretraining*** ") | ||
| # Pretraining | ||
| for i in range(pretrain_gradient_steps): | ||
| pbar.update(i) | ||
| data = offline_buffer.sample() | ||
| # loss | ||
| loss_vals = loss_module(data.to(model_device)) | ||
| # backprop | ||
| transformer_loss = loss_vals["loss"] | ||
|
|
||
| transformer_optim.zero_grad() | ||
| torch.nn.utils.clip_grad_norm_(policy.parameters(), clip_grad) | ||
| transformer_loss.backward() | ||
| transformer_optim.step() | ||
|
|
||
| scheduler.step() | ||
|
|
||
| # evaluation | ||
| with set_exploration_type(ExplorationType.MEAN), torch.no_grad(): | ||
| if i % pretrain_log_interval == 0: | ||
| eval_td = test_env.rollout( | ||
| max_steps=eval_steps, | ||
| policy=inference_policy, | ||
| auto_cast_to_device=True, | ||
| ) | ||
| if r0 is None: | ||
| r0 = eval_td["next", "reward"].sum(1).mean().item() / reward_scaling | ||
| if l0 is None: | ||
| l0 = transformer_loss.item() | ||
|
|
||
| eval_reward = eval_td["next", "reward"].sum(1).mean().item() / reward_scaling | ||
| if logger is not None: | ||
| for key, value in loss_vals.items(): | ||
| logger.log_scalar(key, value.item(), i) | ||
| logger.log_scalar("evaluation reward", eval_reward, i) | ||
|
|
||
| pbar.set_description( | ||
| f"[Pre-Training] loss: {transformer_loss.item(): 4.4f} (init: {l0: 4.4f}), evaluation reward: {eval_reward: 4.4f} (init={r0: 4.4f})" | ||
| ) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| main() |
Oops, something went wrong.