FoX: Formation-aware exploration in multi-agent reinforcement learning

• URL: http://arxiv.org/abs/2308.11272v2
• Date: Sun, 14 Jan 2024 04:46:49 GMT
• Title: FoX: Formation-aware exploration in multi-agent reinforcement learning
• Authors: Yonghyeon Jo, Sunwoo Lee, Junghyuk Yeom, Seungyul Han
• Abstract summary: We propose a formation-based equivalence relation on the exploration space and aim to reduce the search space by exploring only meaningful states in different formations. Numerical results show that the proposed FoX framework significantly outperforms the state-of-the-art MARL algorithms on Google Research Football (GRF) and sparse Starcraft II multi-agent challenge (SMAC) tasks.
• Score: 10.554220876480297
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, deep multi-agent reinforcement learning (MARL) has gained significant popularity due to its success in various cooperative multi-agent tasks. However, exploration still remains a challenging problem in MARL due to the partial observability of the agents and the exploration space that can grow exponentially as the number of agents increases. Firstly, in order to address the scalability issue of the exploration space, we define a formation-based equivalence relation on the exploration space and aim to reduce the search space by exploring only meaningful states in different formations. Then, we propose a novel formation-aware exploration (FoX) framework that encourages partially observable agents to visit the states in diverse formations by guiding them to be well aware of their current formation solely based on their own observations. Numerical results show that the proposed FoX framework significantly outperforms the state-of-the-art MARL algorithms on Google Research Football (GRF) and sparse Starcraft II multi-agent challenge (SMAC) tasks.

Related papers

• Choices are More Important than Efforts: LLM Enables Efficient Multi-Agent Exploration [46.938186139700804]
  This paper introduces LEMAE, choosing to channel informative task-relevant guidance from a knowledgeable Large Language Model (LLM) for Efficient Multi-Agent Exploration. Specifically, we ground linguistic knowledge from LLM into symbolic key states, that are critical for task fulfillment, in a discriminative manner at low inference costs. Benefiting from diminishing redundant explorations, LEMAE outperforms existing SOTA approaches on the challenging benchmarks (e.g., SMAC and MPE) by a large margin, achieving a 10x acceleration in certain scenarios.
  arXiv  Detail & Related papers  (2024-10-03T14:21:23Z)
• From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models [56.9134620424985]
  Cross-modal reasoning (CMR) is increasingly recognized as a crucial capability in the progression toward more sophisticated artificial intelligence systems. The recent trend of deploying Large Language Models (LLMs) to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness. This survey offers a nuanced exposition of current methodologies applied in CMR using LLMs, classifying these into a detailed three-tiered taxonomy.
  arXiv  Detail & Related papers  (2024-09-19T02:51:54Z)
• MESA: Cooperative Meta-Exploration in Multi-Agent Learning through Exploiting State-Action Space Structure [37.56309011441144]
  This paper introduces MESA, a novel meta-exploration method for cooperative multi-agent learning. It learns to explore by first identifying the agents' high-rewarding joint state-action subspace from training tasks and then learning a set of diverse exploration policies to "cover" the subspace. Experiments show that with learned exploration policies, MESA achieves significantly better performance in sparse-reward tasks in several multi-agent particle environments and multi-agent MuJoCo environments.
  arXiv  Detail & Related papers  (2024-05-01T23:19:48Z)
• Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning [27.81925751697255]
  We propose a novel method for efficient multi-agent exploration in complex scenarios. We formulate the imagination as a sequence modeling problem, where the states, observations, prompts, actions, and rewards are predicted autoregressively. By initializing agents at the critical states, IIE significantly increases the likelihood of discovering potentially important underexplored regions.
  arXiv  Detail & Related papers  (2024-02-28T01:45:01Z)
• Strangeness-driven Exploration in Multi-Agent Reinforcement Learning [0.0]
  We introduce a new exploration method with the strangeness that can be easily incorporated into any centralized training and decentralized execution (CTDE)-based MARL algorithms. The exploration bonus is obtained from the strangeness and the proposed exploration method is not much affected by transitions commonly observed in MARL tasks.
  arXiv  Detail & Related papers  (2022-12-27T11:08:49Z)
• Long-Term Exploration in Persistent MDPs [68.8204255655161]
  We propose an exploration method called Rollback-Explore (RbExplore) In this paper, we propose an exploration method called Rollback-Explore (RbExplore), which utilizes the concept of the persistent Markov decision process. We test our algorithm in the hard-exploration Prince of Persia game, without rewards and domain knowledge.
  arXiv  Detail & Related papers  (2021-09-21T13:47:04Z)
• Exploration in Deep Reinforcement Learning: A Comprehensive Survey [24.252352133705735]
  Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Learning (MARL) have achieved significant success across a wide range of domains, such as game AI, autonomous vehicles, robotics and finance. DRL and deep MARL agents are widely known to be sample-inefficient and millions of interactions are usually needed even for relatively simple game settings. This paper provides a comprehensive survey on existing exploration methods in DRL and deep MARL.
  arXiv  Detail & Related papers  (2021-09-14T13:16:33Z)
• Cooperative Exploration for Multi-Agent Deep Reinforcement Learning [127.4746863307944]
  We propose cooperative multi-agent exploration (CMAE) for deep reinforcement learning. The goal is selected from multiple projected state spaces via a normalized entropy-based technique. We demonstrate that CMAE consistently outperforms baselines on various tasks.
  arXiv  Detail & Related papers  (2021-07-23T20:06:32Z)
• Batch Exploration with Examples for Scalable Robotic Reinforcement Learning [63.552788688544254]
  Batch Exploration with Examples (BEE) explores relevant regions of the state-space guided by a modest number of human provided images of important states. BEE is able to tackle challenging vision-based manipulation tasks both in simulation and on a real Franka robot.
  arXiv  Detail & Related papers  (2020-10-22T17:49:25Z)
• UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning [53.73686229912562]
  We propose a novel MARL approach called Universal Value Exploration (UneVEn) UneVEn learns a set of related tasks simultaneously with a linear decomposition of universal successor features. Empirical results on a set of exploration games, challenging cooperative predator-prey tasks requiring significant coordination among agents, and StarCraft II micromanagement benchmarks show that UneVEn can solve tasks where other state-of-the-art MARL methods fail.
  arXiv  Detail & Related papers  (2020-10-06T19:08:47Z)

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.