[Feature, Example] A3C Atari Implementation for TorchRL #3001
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This all looks pretty good!
Could you share a (couple of) learning curve?
Another thing to do before landing is to add it to the sota-implementations CI run:
https://github.com/pytorch/rl/blob/main/.github/unittest/linux_sota/scripts/test_sota.py
Make sure the config passed there is as much barebone as we can - we just want to run the script for a couple of collection / optim iters and make sure it runs without error (not that it properly trains).
We also need to add it to the sota-check runs
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Thanks @vmoens . I'll add the required changes as well as some training curves. |
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@vmoens, I have added the required scripts as well. Not getting enough resources and time for hyperparam tuning to generate a proper training curve. |
| logger = get_logger( | ||
| cfg.logger.backend, | ||
| logger_name="a3c", | ||
| experiment_name=exp_name, | ||
| wandb_kwargs={ | ||
| "config": dict(cfg), | ||
| "project": cfg.logger.project_name, | ||
| "group": cfg.logger.group_name, | ||
| }, | ||
| ) |
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What I usually see is that the logger is only passed to the first worker.
Another thing is that you may want to assume that the logger isn't serializable and should be instantiated locally within the worker.
| num_workers = cfg.multiprocessing.num_workers | ||
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| if num_workers is None: | ||
| num_workers = mp.cpu_count() |
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we should have way fewer workers - I think we need users to tell us how many.
| data_reshape = data.reshape(-1) | ||
| losses = [] | ||
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| mini_batches = data_reshape.split(self.mini_batch_size) |
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To shuffle things a bit I usually rely on a replay buffer instance rather than just splitting the data
| for local_param, global_param in zip( | ||
| self.local_actor.parameters(), self.global_actor.parameters() | ||
| ): | ||
| global_param._grad = local_param.grad | ||
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| for local_param, global_param in zip( | ||
| self.local_critic.parameters(), self.global_critic.parameters() | ||
| ): | ||
| global_param._grad = local_param.grad | ||
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| gn = torch.nn.utils.clip_grad_norm_( | ||
| self.loss_module.parameters(), max_norm=max_grad_norm | ||
| ) |
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can you explain what we do here? What do we use the _grad for?
| torch.set_float32_matmul_precision("high") | ||
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| class SharedAdam(torch.optim.Adam): |
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shouldn't we move this to the utils file?
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Description
Describe your changes in detail.
This PR adds an implementation of the Asynchronous Advantage Actor-Critic (A3C) algorithm for Atari environments in the torchrl/sota-implementations directory. The main files added are:
a3c_atari.py: Contains the A3C worker class, shared optimizer, and main training loop using multiprocessing.
utils_atari.py: Provides utility functions for environment creation, model construction, and evaluation, adapted for Atari tasks.
config_atari.yaml: Configuration file for hyperparameters, environment settings, and logging.
The implementation leverages TorchRL's collectors, objectives, and logging utilities, and is designed to be modular and extensible for research and benchmarking. Some of the utils functions are also borrowed from a2c_atari.
Motivation and Context
This change is required to provide a strong, reproducible baseline for A3C on Atari environments using TorchRL. It enables researchers and practitioners to benchmark and compare reinforcement learning algorithms within the TorchRL ecosystem. The implementation follows best practices for distributed RL and is compatible with TorchRL's API.
This PR solves the issue: #1755
Types of changes
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xin all the boxes that apply.If you are unsure about any of these, don't hesitate to ask. We are here to help!