This repository provides a curated collection of practical "hacks" and debugging tips for training deep reinforcement learning (RL) systems, based on lectures by John Schulman. It's aimed at researchers and engineers working with RL algorithms who need to improve stability, debug performance issues, and reproduce results.

How It Works

The hacks focus on practical strategies for simplifying problems, debugging algorithms, framing tasks, and reproducing research. Key advice includes simplifying state and reward spaces, visualizing random policies, standardizing observations and rewards, and using robust baselines. The repository emphasizes iterative refinement and careful hyperparameter tuning, particularly regarding batch sizes and learning rates.

Quick Start & Requirements

• Install: No explicit installation instructions are provided. The content is primarily textual advice.
• Requirements: Assumes familiarity with deep RL concepts and environments. Access to RL frameworks (e.g., OpenAI Baselines, RLLab) is implied for practical application.

Highlighted Details

• Debugging tips for new algorithms and tasks, including simplifying feature/reward spaces.
• Strategies for framing RL problems, ensuring usable observations and reasonable scaling.
• Guidance on reproducing paper results, emphasizing sample efficiency and hyperparameter tuning.
• Best practices for ongoing training, including hyperparameter sensitivity analysis and benchmarking.

Maintenance & Community

• The repository is based on a 2017 lecture series. There are no indications of recent updates or active community engagement.

Licensing & Compatibility

• The repository does not specify a license.

Limitations & Caveats

The content is derived from a 2017 lecture, and some advice may be outdated given the rapid evolution of RL techniques and frameworks. The lack of code examples or a structured framework limits direct applicability without significant adaptation.