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Adding TRPO #435

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Adding TRPO #435

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Jackory
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@Jackory Jackory commented Nov 30, 2023

Description

TRPO is a representative algorithm of policy gradient in reinforcement learning. Although it is no longer practical, its ideas and mathematical principles are still worth considering. Currently, I haven't seen a single-file implementation of TRPO. I'm here to implement a single-file version of TRPO to help beginners understand it.

Types of changes

  • Bug fix
  • New feature
  • New algorithm
  • Documentation

Checklist:

  • I've read the CONTRIBUTION guide (required).
  • I have ensured pre-commit run --all-files passes (required).
  • I have updated the tests accordingly (if applicable).
  • I have updated the documentation and previewed the changes via mkdocs serve.
    • I have explained note-worthy implementation details.
    • I have explained the logged metrics.
    • I have added links to the original paper and related papers.

If you need to run benchmark experiments for a performance-impacting changes:

  • I have contacted @vwxyzjn to obtain access to the openrlbenchmark W&B team.
  • I have used the benchmark utility to submit the tracked experiments to the openrlbenchmark/cleanrl W&B project, optionally with --capture_video.
  • I have performed RLops with python -m openrlbenchmark.rlops.
    • For new feature or bug fix:
      • I have used the RLops utility to understand the performance impact of the changes and confirmed there is no regression.
    • For new algorithm:
      • I have created a table comparing my results against those from reputable sources (i.e., the original paper or other reference implementation).
    • I have added the learning curves generated by the python -m openrlbenchmark.rlops utility to the documentation.
    • I have added links to the tracked experiments in W&B, generated by python -m openrlbenchmark.rlops ....your_args... --report, to the documentation.

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@vwxyzjn
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vwxyzjn commented Dec 18, 2023

Hi this is some cool stuff! Feel free to run some benchmarks with mujoco to see how it performs.

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2 participants