Cooperative Reward Shaping for Multi-Agent Pathfinding

• URL: http://arxiv.org/abs/2407.10403v1
• Date: Mon, 15 Jul 2024 02:44:41 GMT
• Title: Cooperative Reward Shaping for Multi-Agent Pathfinding
• Authors: Zhenyu Song, Ronghao Zheng, Senlin Zhang, Meiqin Liu,
• Abstract summary: The primary objective of Multi-Agent Pathfinding (MAPF) is to plan efficient and conflict-free paths for all agents. Traditional multi-agent path planning algorithms struggle to achieve efficient distributed path planning for multiple agents. This letter introduces a unique reward shaping technique based on Independent Q-Learning (IQL)
• Score: 4.244426154524592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The primary objective of Multi-Agent Pathfinding (MAPF) is to plan efficient and conflict-free paths for all agents. Traditional multi-agent path planning algorithms struggle to achieve efficient distributed path planning for multiple agents. In contrast, Multi-Agent Reinforcement Learning (MARL) has been demonstrated as an effective approach to achieve this objective. By modeling the MAPF problem as a MARL problem, agents can achieve efficient path planning and collision avoidance through distributed strategies under partial observation. However, MARL strategies often lack cooperation among agents due to the absence of global information, which subsequently leads to reduced MAPF efficiency. To address this challenge, this letter introduces a unique reward shaping technique based on Independent Q-Learning (IQL). The aim of this method is to evaluate the influence of one agent on its neighbors and integrate such an interaction into the reward function, leading to active cooperation among agents. This reward shaping method facilitates cooperation among agents while operating in a distributed manner. The proposed approach has been evaluated through experiments across various scenarios with different scales and agent counts. The results are compared with those from other state-of-the-art (SOTA) planners. The evidence suggests that the approach proposed in this letter parallels other planners in numerous aspects, and outperforms them in scenarios featuring a large number of agents.

Related papers

• From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning [62.54484062185869]
  We introduce StepAgent, which utilizes step-wise reward to optimize the agent's reinforcement learning process. We propose implicit-reward and inverse reinforcement learning techniques to facilitate agent reflection and policy adjustment.
  arXiv  Detail & Related papers  (2024-11-06T10:35:11Z)
• Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
  We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm. HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies. HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
  arXiv  Detail & Related papers  (2024-06-12T08:48:06Z)
• Joint Intrinsic Motivation for Coordinated Exploration in Multi-Agent Deep Reinforcement Learning [0.0]
  We propose an approach for rewarding strategies where agents collectively exhibit novel behaviors. Jim rewards joint trajectories based on a centralized measure of novelty designed to function in continuous environments. Results show that joint exploration is crucial for solving tasks where the optimal strategy requires a high level of coordination.
  arXiv  Detail & Related papers  (2024-02-06T13:02:00Z)
• Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing [11.639503711252663]
  We tackle the multi-agent active hypothesis testing (AHT) problem by introducing a novel algorithm rooted in the framework of deep multi-agent reinforcement learning. We present a comprehensive set of experimental results that effectively showcase the agents' ability to learn collaborative strategies and enhance performance.
  arXiv  Detail & Related papers  (2023-09-14T01:18:04Z)
• Mimicking Better by Matching the Approximate Action Distribution [48.95048003354255]
  We introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations. We show that it requires considerable fewer interactions to achieve expert performance, outperforming current state-of-the-art on-policy methods.
  arXiv  Detail & Related papers  (2023-06-16T12:43:47Z)
• Learning Reward Machines in Cooperative Multi-Agent Tasks [75.79805204646428]
  This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL) It combines cooperative task decomposition with the learning of reward machines (RMs) encoding the structure of the sub-tasks. The proposed method helps deal with the non-Markovian nature of the rewards in partially observable environments.
  arXiv  Detail & Related papers  (2023-03-24T15:12:28Z)
• Learning From Good Trajectories in Offline Multi-Agent Reinforcement Learning [98.07495732562654]
  offline multi-agent reinforcement learning (MARL) aims to learn effective multi-agent policies from pre-collected datasets. One agent learned by offline MARL often inherits this random policy, jeopardizing the performance of the entire team. We propose a novel framework called Shared Individual Trajectories (SIT) to address this problem.
  arXiv  Detail & Related papers  (2022-11-28T18:11:26Z)
• Subdimensional Expansion Using Attention-Based Learning For Multi-Agent Path Finding [9.2127262112464]
  Multi-Agent Path Finding (MAPF) finds conflict-free paths for multiple agents from their respective start to goal locations. We develop a novel multi-agent planner called LM* by integrating this learning-based single-agent planner with M*. Our results show that for both "seen" and "unseen" maps, in comparison with M*, LM* has fewer conflicts to be resolved and thus, runs faster and enjoys higher success rates.
  arXiv  Detail & Related papers  (2021-09-29T20:01:04Z)
• Scalable, Decentralized Multi-Agent Reinforcement Learning Methods Inspired by Stigmergy and Ant Colonies [0.0]
  We investigate a novel approach to decentralized multi-agent learning and planning. In particular, this method is inspired by the cohesion, coordination, and behavior of ant colonies. The approach combines single-agent RL and an ant-colony-inspired decentralized, stigmergic algorithm for multi-agent path planning and environment modification.
  arXiv  Detail & Related papers  (2021-05-08T01:04:51Z)
• Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions [10.354181009277623]
  Multi-agent path finding (MAPF) determines an ensemble of collision-free paths for multiple agents between their respective start and goal locations. This article presents a natural generalization of MAPF with asynchronous actions where agents do not necessarily start and stop concurrently.
  arXiv  Detail & Related papers  (2021-03-08T02:34:17Z)
• Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic [54.2180984002807]
  Multi-agent adversarial inverse reinforcement learning (MA-AIRL) is a recent approach that applies single-agent AIRL to multi-agent problems. We propose a multi-agent inverse RL algorithm that is more sample-efficient and scalable than previous works.
  arXiv  Detail & Related papers  (2020-02-24T20:30:45Z)

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.