marl-jax: Multi-Agent Reinforcement Leaning Framework

• URL: http://arxiv.org/abs/2303.13808v2
• Date: Tue, 25 Jul 2023 16:12:01 GMT
• Title: marl-jax: Multi-Agent Reinforcement Leaning Framework
• Authors: Kinal Mehta, Anuj Mahajan, Pawan Kumar
• Abstract summary: We present marl-jax, a multi-agent reinforcement learning software package for training and evaluating social generalization of the agents. The package is designed for training a population of agents in multi-agent environments and evaluating their ability to generalize to diverse background agents.
• Score: 7.064383217512461
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Reinforcement Learning (RL) have led to many exciting applications. These advancements have been driven by improvements in both algorithms and engineering, which have resulted in faster training of RL agents. We present marl-jax, a multi-agent reinforcement learning software package for training and evaluating social generalization of the agents. The package is designed for training a population of agents in multi-agent environments and evaluating their ability to generalize to diverse background agents. It is built on top of DeepMind's JAX ecosystem~\cite{deepmind2020jax} and leverages the RL ecosystem developed by DeepMind. Our framework marl-jax is capable of working in cooperative and competitive, simultaneous-acting environments with multiple agents. The package offers an intuitive and user-friendly command-line interface for training a population and evaluating its generalization capabilities. In conclusion, marl-jax provides a valuable resource for researchers interested in exploring social generalization in the context of MARL. The open-source code for marl-jax is available at: \href{https://github.com/kinalmehta/marl-jax}{https://github.com/kinalmehta/marl-jax}

Related papers

• OpenHands: An Open Platform for AI Software Developers as Generalist Agents [109.8507367518992]
  We introduce OpenHands, a platform for the development of AI agents that interact with the world in similar ways to a human developer. We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, and incorporation of evaluation benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T17:50:43Z)
• CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents [49.68117560675367]
  Crab is the first benchmark framework designed to support cross-environment tasks. Our framework supports multiple devices and can be easily extended to any environment with a Python interface. The experimental results demonstrate that the single agent with GPT-4o achieves the best completion ratio of 38.01%.
  arXiv  Detail & Related papers  (2024-07-01T17:55:04Z)
• AgentGym: Evolving Large Language Model-based Agents across Diverse Environments [116.97648507802926]
  Large language models (LLMs) are considered a promising foundation to build such agents. We take the first step towards building generally-capable LLM-based agents with self-evolution ability. We propose AgentGym, a new framework featuring a variety of environments and tasks for broad, real-time, uni-format, and concurrent agent exploration.
  arXiv  Detail & Related papers  (2024-06-06T15:15:41Z)
• PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning [20.746383793882984]
  Training for multi-agent reinforcement learning(MARL) is a time-consuming process. One drawback is that strategy of each agent in MARL is independent but actually in cooperation. We propose three simple approaches called Average Sharing(A-PPS), Reward-Scalability Periodically and Partial Personalized Periodically.
  arXiv  Detail & Related papers  (2024-03-05T03:59:01Z)
• XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library [18.603206638756056]
  XuanCe is a comprehensive and unified deep reinforcement learning (DRL) library. XuanCe offers a wide range of functionalities, including over 40 classical DRL and multi-agent DRL algorithms. XuanCe is open-source and can be accessed at https://agi-brain.com/agi-brain/xuance.git.
  arXiv  Detail & Related papers  (2023-12-25T14:45:39Z)
• JaxMARL: Multi-Agent RL Environments and Algorithms in JAX [105.343918678781]
  We present JaxMARL, the first open-source, Python-based library that combines GPU-enabled efficiency with support for a large number of commonly used MARL environments. Our experiments show that, in terms of wall clock time, our JAX-based training pipeline is around 14 times faster than existing approaches. We also introduce and benchmark SMAX, a JAX-based approximate reimplementation of the popular StarCraft Multi-Agent Challenge.
  arXiv  Detail & Related papers  (2023-11-16T18:58:43Z)
• Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations [53.76682562935373]
  We introduce an efficient framework called textbfInteRecAgent, which employs LLMs as the brain and recommender models as tools. InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.
  arXiv  Detail & Related papers  (2023-08-31T07:36:44Z)
• An Empirical Study on Google Research Football Multi-agent Scenarios [30.926070192524193]
  We open-source our training framework Light-MALib which extends the MALib by distributed and asynchronized implementation with additional analytical tools for football games. We provide guidance for building strong football AI with population-based training and release diverse pretrained policies for benchmarking.
  arXiv  Detail & Related papers  (2023-05-16T14:18:53Z)
• Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration [35.75029940279768]
  We study multi-agent reinforcement learning in the most basic cooperative setting -- Markov teams. We propose an algorithm in which each agent independently runs a stage-based V-learning style algorithm. We show that the agents can learn an $epsilon$-approximate Nash equilibrium policy in at most $proptowidetildeO (1/epsilon4)$ episodes.
  arXiv  Detail & Related papers  (2021-10-12T02:45:12Z)
• MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning [61.28547338576706]
  Population-based multi-agent reinforcement learning (PB-MARL) refers to the series of methods nested with reinforcement learning (RL) algorithms. We present MALib, a scalable and efficient computing framework for PB-MARL.
  arXiv  Detail & Related papers  (2021-06-05T03:27:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.