LLM-VM
★ 491
vs
on-policy
★ 2.0k
LLM-VM vs on-policy
LLM-VM: The Anarchy LLM-VM is an optimized backend designed to run open-source LLMs with modern features like tool usage and persistent memory. It acts as a virtual machine for human language, coordinating models, data, prompts, and tools to optimize batch calls and support various architectures.; 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 LLM-VM if…Accelerate AGI development and prototyping
Choose on-policy if…Research and experimentation in cooperative multi-agent reinforcement learning
02
Side-by-Side Comparison
03
Features
LLM-VM01Implicit Agents
02Inference Optimization
03Task Auto-Optimization
04Library Callable
05HTTP Endpoints
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
LLM-VM↳Accelerate AGI development and prototyping
↳Reduce costs for running and testing LLM models locally
↳Flexibly switch and evaluate different open-source LLM models
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
LLM-VMTrending
on-policyTrendingReinforcement LearningMulti-Agent AI
FAQ
FAQ
What is the difference between LLM-VM and on-policy?
Both LLM-VM and on-policy are in the LLM Infra category. LLM-VM has 491 stars, while on-policy has 2.0k stars.
Which is better, LLM-VM or on-policy?
The best choice depends on your use case. Choose LLM-VM if Accelerate AGI development and prototyping, and on-policy if Research and experimentation in cooperative multi-agent reinforcement learning.
Is LLM-VM free or open source?
Yes, LLM-VM 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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