on-policy
★ 2.0k
vs
mini-swe-agent
★ 4.7k
on-policy vs mini-swe-agent
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.; mini-swe-agent: Mini-SWE-agent is a lightweight, 100-line AI agent designed to solve GitHub issues and more, offering a simplified yet performant alternative to larger coding agents. It focuses on minimalism, high performance on benchmarks like SWE-bench, and easy deployment across various environments.
01
TL;DR
Choose on-policy if…Research and experimentation in cooperative multi-agent reinforcement learning
Choose mini-swe-agent if…Researchers for benchmarking, fine-tuning, or RL experiments without bloat
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
mini-swe-agent01Minimal code (approx. 100 lines of Python)
02High performance (>74% on SWE-bench verified benchmark)
03Easy deployment and sandboxing (Docker, Podman, Singularity)
04Utilizes only Bash tools, avoiding complex tool-calling interfaces
05Linear history for simplified debugging and fine-tuning
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
mini-swe-agent↳Researchers for benchmarking, fine-tuning, or RL experiments without bloat
↳Developers who want to own, understand, and modify their AI tools
↳Engineers needing a trivial-to-sandbox and deployable solution anywhere
05
Best For
on-policyTrendingReinforcement LearningMulti-Agent AI
mini-swe-agentTrendingHidden Gem
FAQ
FAQ
What is the difference between on-policy and mini-swe-agent?
Both on-policy and mini-swe-agent are in the LLM Infra category. on-policy has 2.0k stars, while mini-swe-agent has 4.7k stars.
Which is better, on-policy or mini-swe-agent?
The best choice depends on your use case. Choose on-policy if Research and experimentation in cooperative multi-agent reinforcement learning, and mini-swe-agent if Researchers for benchmarking, fine-tuning, or RL experiments without bloat.
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
Is mini-swe-agent free or open source?
Yes, mini-swe-agent is open source on GitHub.
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