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.
TL;DR
Research and experimentation in cooperative multi-agent reinforcement learning
Developing and testing new Reinforcement Learning algorithms
Side-by-Side Comparison
Features
on-policyskrlUse Cases
on-policyskrlBest For
on-policyskrlFAQ
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