on-policy
★ 2.1k
vs
wassette
★ 942
on-policy vs wassette
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.; wassette: Wassette is a security-oriented runtime that executes WebAssembly Components via MCP, making it easy to extend AI agents with new tools directly from a chat window. It leverages the Wasmtime security sandbox to provide browser-grade isolation, ensuring that these reusable components run safely.
01
TL;DR
Choose on-policy if…Research and experimentation in cooperative multi-agent reinforcement learning
Choose wassette if…Integrating new capabilities like time-telling into AI agents using WebAssembly components.
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
wassette01Conveniently extend AI agents with new tools without leaving the chat interface.
02Supports generic, reusable Wasm Components that are not MCP-specific.
03Provides browser-grade isolation and security through the Wasmtime security sandbox.
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
wassette↳Integrating new capabilities like time-telling into AI agents using WebAssembly components.
↳Registering Wassette with various AI agents such as GitHub Copilot and Claude Code for enhanced functionality.
↳Enabling secure execution of third-party tools within an agent's environment.
05
Best For
on-policyTrendingReinforcement LearningMulti-Agent AI
wassetteTrendingEssential
FAQ
FAQ
What is the difference between on-policy and wassette?
Both on-policy and wassette are in the LLM Infra category. on-policy has 2.1k stars, while wassette has 942 stars.
Which is better, on-policy or wassette?
The best choice depends on your use case. Choose on-policy if Research and experimentation in cooperative multi-agent reinforcement learning, and wassette if Integrating new capabilities like time-telling into AI agents using WebAssembly components..
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
Is wassette free or open source?
Yes, wassette is open source on GitHub (MIT).
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