Jax c51 contrib

[kinalmehta]

Description

JAX implementation for C51 Implementation for #221

Types of changes

• [ ] Bug fix
• [ ] New feature
• [x] New algorithm
• [x] Documentation

Checklist:

• [x] I've read the CONTRIBUTION guide (required).
• [x] I have ensured pre-commit run --all-files passes (required).
• [x] I have updated the documentation and previewed the changes via mkdocs serve.
• [ ] I have updated the tests accordingly (if applicable).

If you are adding new algorithms or your change could result in performance difference, you may need to (re-)run tracked experiments. See https://github.com/vwxyzjn/cleanrl/pull/137 as an example PR.

• [x] I have contacted @vwxyzjn to obtain access to the openrlbenchmark W&B team (required).
• [x] I have tracked applicable experiments in openrlbenchmark/cleanrl with --capture-video flag toggled on (required).
• [x] I have added additional documentation and previewed the changes via mkdocs serve.
  • [x] I have explained note-worthy implementation details.
  • [x] I have explained the logged metrics.
  • [ ] I have added links to the original paper and related papers (if applicable).
  • [ ] I have added links to the PR related to the algorithm.
  • [x] I have created a table comparing my results against those from reputable sources (i.e., the original paper or other reference implementation).
  • [x] I have added the learning curves (in PNG format with width=500 and height=300).
  • [x] I have added links to the tracked experiments.
• [ ] I have updated the tests accordingly (if applicable).

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Name	Status	Preview	Updated
cleanrl	✅ Ready (Inspect)	Visit Preview	Dec 30, 2022 at 5:23PM (UTC)

[kinalmehta]

Results on classical gym environments can be checked here. https://wandb.ai/kinalmehta/jax-cleanrl/reports/C51-JAX-vs-Pytorch-on-Classical-Gym-Environments--VmlldzoyNDQ3OTk5

We see a speed-up of about ~30% in the JAX version compared to Pytorch.

[kinalmehta]

Here is the benchmark report on atari environments https://wandb.ai/kinalmehta/jax-cleanrl/reports/C51-JAX-vs-Pytorch-on-Atari-Environments--VmlldzoyNjkyNDY0

Important observations:

• BeamRider performance is bad compared PyTorch version
• Breakout performance almost matches PyTorch variant but is still a bit low comparatively
• For Pong, the performance matches perfectly for 2 seeds, but the reward remains zero for one of the seeds.

Need to look into more detail about the differences between PyTorch and JAX implementations to fix the above mentioned issues.

[joaogui1]

How does it compare to Dopamine's version?

[kinalmehta]

How does it compare to Dopamine's version? I haven't checked Dopamine yet. I will have a look and update here, though it might take some time.

[vwxyzjn]

FYI dopamine has a benchmark, but its x-axis is not the environment steps... Any clue on how we can compare those results? @joaogui1

[kinalmehta]

Atari Fixed

After months of procrastination and debugging various aspects, I finally stumbled upon the cause of performance degradation. The incorrect epsilon value caused this performance degradation. I missed this detail and used the default value $10^{-8}$ from optax. However, the C51-PyTorch version uses ${0.01}/{batch\_size}$. Hoowever I couldn't find any motivation for using this value.

Reading up more on this led to the conclusion that this is a common issue even in NLP and CV as well. More about this hyperparameter can be read here.

Benchmarking classical envs on CPU

I have updated the plots of classical gym environments (CartPole, Acrobot, MountainCar) by benchmarking on CPU. We see significant speed-up compared to pytorch version on CPU.

Comparison with dopamine

Based on the beamrider plot shared above, the below table summarizes the final score comparison	implementation	score
dopamine	5000-7000
cleanrl-pytorch	~9500
cleanrl-jax-old	~2500
cleanrl-jax-fixed	~9500

Reports link

• https://wandb.ai/kinalmehta/cleanrl/reports/Regression-Report-c51_atari_jax--VmlldzozMjM1Mzc1
• https://wandb.ai/kinalmehta/cleanrl/reports/Regression-Report-c51_jax--VmlldzozMjM1MzM0

Conclusion

The updated plots are available on the above links itself. The PR looks good to be mearged once the documentation is updated. Anything else I am missing here @vwxyzjn?

[vwxyzjn]

The results look incredible. Great job @kinalmehta. Thanks for chasing down the cause for the issue. The code also look great to me. Feel free to start adding documentation. You should also move the experiments to the openrlbenchmark/cleanrl namespace.

[kinalmehta]

I've added the documentation, and now I believe this PR is ready for the final review.