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Deep RL reference: algorithms, method and debugging v1.0.0

Use this resource to understand or debug a deep reinforcement learning algorithm

New

  • 539 downloads
  • 220 likes
  • 106 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install deep-rl-reference

With no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.

First time: connect the CLI with

npx --yes palatekit@latest login

The agent applies the Spinning Up frameworks: Markov decision processes, the model-free algorithm taxonomy, policy gradients, the six reference algorithms (VPG, TRPO, PPO, DDPG, TD3, SAC) and rules for multi-seed experiments. It loads only the chapter you need.

Mostly picked by

Updated November 2, 2026

Card for the “Deep RL reference: algorithms, method and debugging” skill

Details

Works in
Install guides
Generates with
Your agent's model
Licence
MIT

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What you get

  • SKILL.md: core frameworks, algorithm taxonomy and chapter index
  • chapters/: twelve chapters, from installation to key papers and exercises

Good fit for

  • Your PPO agent does not learn and you cannot see why
  • You want to understand the difference between policy optimisation and Q-learning

Not for

    Tags

    • reinforcement learning
    • PPO
    • SAC
    • debugging

    About

    You say

    “Explain why my PPO does not converge and what to check in the loss function.”

    Card for the “Deep RL reference: algorithms, method and debugging” skill

    Skill published by alirezarezvani under the MIT license, delivered in its original version.

    Show more

    How to use it

    1. Install the skill in your agent's skills folder
    2. Ask your agent to understand or debug a deep reinforcement learning algorithm
    3. Review and adjust the result

    Content preview

    Files

    • chapters/ch01-introduction.md6.1 KB
    • chapters/ch02-installation.md3.9 KB
    • chapters/ch03-algorithm-lineup.md5.8 KB
    • chapters/ch04-running-experiments.md6.2 KB
    • chapters/ch05-experiment-outputs.md4.6 KB
    • chapters/ch06-plotting-results.md3.9 KB
    • chapters/ch07-key-concepts-in-rl.md8 KB
    • chapters/ch08-kinds-of-rl-algorithms.md8.1 KB
    • chapters/ch09-intro-to-policy-optimization.md8.7 KB
    • chapters/ch10-spinning-up-as-a-researcher.md10.3 KB
    • chapters/ch11-key-papers-in-deep-rl.md4.6 KB
    • chapters/ch12-exercises.md5.5 KB
    • chapters/ch13-benchmarks.md5 KB
    • chapters/ch14-vpg.md4.9 KB
    • chapters/ch15-trpo.md6 KB
    • chapters/ch16-ppo.md7 KB
    • chapters/ch17-ddpg.md7.5 KB
    • chapters/ch18-td3.md6 KB
    • chapters/ch19-sac.md7.8 KB
    • chapters/ch20-logger-and-utilities.md4.8 KB
    • cheatsheet.md5 KB
    • glossary.md5.4 KB
    • LICENSE1.1 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • patterns.md7.9 KB
    • SKILL.md11.1 KB

    Origin and licence

    Listed unmodified

    Author
    alirezarezvani
    Source
    github.com/alirezarezvani/claude-skills/tree/5434623a788ec3105d238328cfe554e1e2242ff7/engineering/spinning-up-deep-rl/skills/spinning-up-deep-rl
    Licence
    MIT

    Notice

    « spinning-up-deep-rl » (alirezarezvani, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

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