on-policy
★ 2.1k
vs
LLM-Agents-Ecosystem-Handbook
★ 536
on-policy vs LLM-Agents-Ecosystem-Handbook
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.; LLM-Agents-Ecosystem-Handbook: This handbook provides a production-oriented guide for building, evaluating, securing, and deploying modern LLM agent systems. It covers the entire agent stack, including concepts, provider and skill ecosystems, prompt engineering, memory, safety, and observability, along with templates and runnable examples.
01
TL;DR
Choose on-policy if…Research and experimentation in cooperative multi-agent reinforcement learning
Choose LLM-Agents-Ecosystem-Handbook if…Designing and building LLM agent systems from scratch
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
LLM-Agents-Ecosystem-Handbook01Comprehensive Agent Stack Concepts and Design Guides
02Extensive LLM Provider Ecosystem and Routing
03Structured Skill Development and Management
04Advanced Prompt Engineering Techniques for Agents
05Production-Oriented Safety, Observability, and Evaluation Tools
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
LLM-Agents-Ecosystem-Handbook↳Designing and building LLM agent systems from scratch
↳Integrating and managing diverse LLM providers
↳Developing, categorizing, and deploying agent skills
↳Implementing robust safety features and guardrails for agents
↳Establishing evaluation and observability pipelines for agent performance
05
Best For
on-policyTrendingReinforcement LearningMulti-Agent AI
LLM-Agents-Ecosystem-HandbookHidden Gem
FAQ
FAQ
What is the difference between on-policy and LLM-Agents-Ecosystem-Handbook?
Both on-policy and LLM-Agents-Ecosystem-Handbook are in the LLM Infra category. on-policy has 2.1k stars, while LLM-Agents-Ecosystem-Handbook has 536 stars.
Which is better, on-policy or LLM-Agents-Ecosystem-Handbook?
The best choice depends on your use case. Choose on-policy if Research and experimentation in cooperative multi-agent reinforcement learning, and LLM-Agents-Ecosystem-Handbook if Designing and building LLM agent systems from scratch.
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
Is LLM-Agents-Ecosystem-Handbook free or open source?
Yes, LLM-Agents-Ecosystem-Handbook is open source on GitHub (MIT).
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