on-policy
★ 2.0k
vs
skrl
★ 1.1k
on-policy vs skrl
on-policy: This repository implements MAPPO, a multi-agent variant of PPO, widely used in cooperative multi-agent games and research. It provides robust implementations for various multi-agent environments like StarCraft II, Hanabi, and Google Research Football, along with detailed training scripts and hyperparameter guidance.; skrl: skrl is an open-source, modular Reinforcement Learning library implemented in Python, supporting PyTorch, JAX, and NVIDIA Warp. It focuses on modularity, readability, simplicity, and transparent algorithm implementation, also supporting various environment interfaces like Gym, Gymnasium, and Isaac Lab.
01
TL;DR
Choose on-policy if…Research and experimentation in cooperative multi-agent reinforcement learning
Choose skrl if…Developing and testing new Reinforcement Learning algorithms
02
Side-by-Side Comparison
03
Features
on-policy01Implementation of MAPPO (Multi-Agent PPO)
02Support for diverse multi-agent environments (e.g., StarCraft II, Hanabi)
03Ready-to-use training scripts for various scenarios
04Detailed hyperparameter guidance and updated results
05Default support for shared policy among agents
skrl01Modular and extensible design
02Transparent algorithm implementation
03Multi-framework support (PyTorch, JAX, NVIDIA Warp)
04Compatibility with various environment interfaces (Gym, Gymnasium, PettingZoo, ManiSkill)
05Simultaneous training in NVIDIA Isaac Lab and MuJoCo Playground with scope-based resource sharing
04
Use Cases
on-policy↳Research and experimentation in cooperative multi-agent reinforcement learning
↳Benchmarking and evaluating PPO's effectiveness in MARL scenarios
↳Training AI agents for popular multi-agent games like StarCraft II and Hanabi
skrl↳Developing and testing new Reinforcement Learning algorithms
↳Training AI agents in various simulated environments (e.g., robotic control, game AI)
↳Research in Reinforcement Learning leveraging multiple backend frameworks
05
Best For
on-policyTrendingReinforcement LearningMulti-Agent AI
skrlTrendingEssential
FAQ
FAQ
What is the difference between on-policy and skrl?
Both on-policy and skrl are in the LLM Infra category. on-policy has 2.0k stars, while skrl has 1.1k stars.
Which is better, on-policy or skrl?
The best choice depends on your use case. Choose on-policy if Research and experimentation in cooperative multi-agent reinforcement learning, and skrl if Developing and testing new Reinforcement Learning algorithms.
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
Is skrl free or open source?
Yes, skrl is open source on GitHub.
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