AgileRL
★ 921
vs
on-policy
★ 2.0k
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.
01
TL;DR
Choose AgileRL if…Training single-agent tasks in standard Gymnasium environments.
Choose on-policy if…Research and experimentation in cooperative multi-agent reinforcement learning
02
Side-by-Side Comparison
03
Features
AgileRL01RLOps integration for streamlined reinforcement learning development.
02Pioneering evolutionary hyperparameter optimization (HPO) techniques.
03Comprehensive suite of evolvable on-policy, off-policy, offline, multi-agent, and contextual multi-armed bandit algorithms.
04Support for distributed training.
05Algorithms for Large Language Model (LLM) finetuning.
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
04
Use Cases
AgileRL↳Training single-agent tasks in standard Gymnasium environments.
↳Developing multi-agent reinforcement learning solutions in PettingZoo environments.
↳Fine-tuning Large Language Models (LLMs) with reinforcement learning algorithms.
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
05
Best For
AgileRLTrendingHidden Gem
on-policyTrendingReinforcement LearningMulti-Agent AI
FAQ
FAQ
What is the difference between AgileRL and on-policy?
Both AgileRL and on-policy are in the LLM Infra category. AgileRL has 921 stars, while on-policy has 2.0k stars.
Which is better, AgileRL or on-policy?
The best choice depends on your use case. Choose AgileRL if Training single-agent tasks in standard Gymnasium environments., and on-policy if Research and experimentation in cooperative multi-agent reinforcement learning.
Is AgileRL free or open source?
Yes, AgileRL is open source on GitHub.
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
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