on-policy
★ 2.0k
vs
rllm
★ 5.6k
on-policy vs rllm
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.; rllm: rLLM is an open-source framework designed for post-training language agents using reinforcement learning. It allows users to easily build, train, and deploy custom agents and environments for real-world workloads.
01
TL;DR
Choose on-policy if…Research and experimentation in cooperative multi-agent reinforcement learning
Choose rllm if…Training powerful coding models for tasks like code generation and bug fixing.
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
rllm01Open-source framework for reinforcement learning-based post-training of language agents.
02Supports building, training, and deploying custom agents and environments.
03Offers multiple training backends including 'verl' and 'tinker'.
04Enables LoRA and VLM training for advanced models.
05Includes AgentWorkflowEngine for training over arbitrary agentic programs.
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
rllm↳Training powerful coding models for tasks like code generation and bug fixing.
↳Developing sophisticated software engineering agents for automated tasks.
↳Building and evaluating multi-agent systems using reinforcement learning techniques.
05
Best For
on-policyTrendingReinforcement LearningMulti-Agent AI
rllmTrending
FAQ
FAQ
What is the difference between on-policy and rllm?
Both on-policy and rllm are in the LLM Infra category. on-policy has 2.0k stars, while rllm has 5.6k stars.
Which is better, on-policy or rllm?
The best choice depends on your use case. Choose on-policy if Research and experimentation in cooperative multi-agent reinforcement learning, and rllm if Training powerful coding models for tasks like code generation and bug fixing..
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
Is rllm free or open source?
Yes, rllm is open source on GitHub (Apache-2.0).
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