on-policy
★ 2.0k
vs
xLAM
★ 621
on-policy vs xLAM
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.; xLAM: xLAM is a research repository for Large Action Models (LAMs), which aggregates and unifies agent trajectories from diverse environments into a consistent format. It streamlines the creation of a generic data loader optimized for agent training, enabling robust model development across various scenarios.
01
TL;DR
Choose on-policy if…Research and experimentation in cooperative multi-agent reinforcement learning
Choose xLAM if…Function calling in LLMs
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
xLAM01Aggregates agent trajectories from distinct environments
02Standardizes and unifies trajectories into a consistent format
03Optimized generic data loader for agent training
04Maintains equilibrium across different data sources during training
05Supports efficient inference with Transformers and vLLM
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
xLAM↳Function calling in LLMs
↳Training autonomous agents
↳Multi-turn conversation processing
05
Best For
on-policyTrendingReinforcement LearningMulti-Agent AI
xLAMTrending
FAQ
FAQ
What is the difference between on-policy and xLAM?
Both on-policy and xLAM are in the LLM Infra category. on-policy has 2.0k stars, while xLAM has 621 stars.
Which is better, on-policy or xLAM?
The best choice depends on your use case. Choose on-policy if Research and experimentation in cooperative multi-agent reinforcement learning, and xLAM if Function calling in LLMs.
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
Is xLAM free or open source?
Yes, xLAM is open source on GitHub (APACHE).
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