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bio-pufferlib

Train reinforcement learning agents with PuffeRL and PufferEnv APIs.

Updated Mar 13, 2026
Search Tags:#training#high-performance#reinforcement learning#vectorization#rl#environment simulation
One-click install
npx skills add https://github.com/biomaps-infra/blender-opencode --skill bio-pufferlib
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-pufferlib
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/bio-pufferlib
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill bio-pufferlib

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill significantly speeds up reinforcement learning (RL) training by providing a highly optimized framework for environment simulation and agent training, enabling faster experimentation and development.

Core Features & Use Cases

  • High-Performance Training: Achieve millions of steps per second with the PuffeRL PPO+LSTM trainer.
  • Environment Development: Create custom, high-performance environments using the PufferEnv API.
  • Seamless Integration: Works with Gymnasium, PettingZoo, Atari, Procgen, and many other RL environments.
  • Use Case: Train complex multi-agent RL systems or fine-tune agents on large-scale benchmarks much faster than with standard libraries.

Quick Start

Use the bio-pufferlib skill to train an RL agent on the 'procgen-coinrun' environment using default settings.

Frequently Asked Questions about bio-pufferlib

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I speed up reinforcement learning training for large-scale environments?▼

You can speed up reinforcement learning training by using a high-performance framework optimized for parallel environment simulation and vectorized training, achieving millions of steps per second.

Can I use this framework with existing Gymnasium and PettingZoo environments?▼

Yes, this framework seamlessly integrates with existing Gymnasium, PettingZoo, Atari, and Procgen environments. You can train complex multi-agent systems or fine-tune agents on these standard benchmarks without needing to rewrite your environment logic.

What is the best way to build custom high-performance environments for multi-agent RL?▼

Building custom high-performance environments for multi-agent RL is best achieved using the PufferEnv API. It facilitates fast parallel environment simulation and allows you to create custom, high-performance environments tailored for scale.

Does the PuffeRL trainer support vectorized training for multi-agent systems?▼

Yes, the PuffeRL PPO+LSTM trainer fully supports vectorized training for multi-agent systems. It enables fast parallel environment simulation, allowing you to train complex multi-agent reinforcement learning systems at scale.

Why does my reinforcement learning training take so long on standard libraries?▼

Standard libraries often lack optimized parallel environment simulation and vectorized training capabilities. Switching to a high-performance framework with the PuffeRL trainer allows you to process millions of steps per second, drastically reducing training time.

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