AgileRL vs on-policy
AgileRL: AgileRL is a Deep Reinforcement Learning library that streamlines development by introducing RLOps, or MLOps for reinforcement learning. It significantly reduces training time and hyperparameter optimization using pioneering evolutionary techniques, offering up to 10x faster optimization than state-of-the-art methods.; 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.
TL;DR
Training single-agent tasks in standard Gymnasium environments.
Research and experimentation in cooperative multi-agent reinforcement learning
Side-by-Side Comparison
AgileRLon-policyFeatures
AgileRLon-policyUse Cases
AgileRLon-policyBest For
AgileRLon-policyFAQ
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