An Optimal Rewiring Strategy for Reinforcement Social Learning in Cooperative Multiagent Systems

2018-05-13 · Hongyao Tang, Li Wang, Zan Wang, Tim Baarslag, Jianye Hao

Multiagent coordination in cooperative multiagent systems (MASs) has been widely studied in both fixed-agent repeated interaction setting and the static social learning framework. However, two aspects of dynamics in real-world multiagent scenarios are currently missing in existing works. First, the network topologies can be dynamic where agents may change their connections through rewiring during the course of interactions. Second, the game matrix between each pair of agents may not be static and usually not known as a prior. Both the network dynamic and game uncertainty increase the coordination difficulty among agents. In this paper, we consider a multiagent dynamic social learning environment in which each agent can choose to rewire potential partners and interact with randomly chosen neighbors in each round. We propose an optimal rewiring strategy for agents to select most beneficial peers to interact with for the purpose of maximizing the accumulated payoff in repeated interactions. We empirically demonstrate the effectiveness and robustness of our approach through comparing with benchmark strategies. The performance of three representative learning strategies under our social learning framework with our optimal rewiring is investigated as well.

📄 PDF Abstract BibTeX arXiv:1805.08588

Code (0)

등록된 구현이 없습니다.

Q-Learning-Driven Adaptive Rewiring for Cooperative Control in Heterogeneous Networks

2025-09-01 · Yi-Ning Weng, Hsuan-Wei Lee arxiv

Cooperation emergence in multi-agent systems represents a fundamental statistical physics problem where microscopic learning rules drive macroscopic collective behavior transitions. We propose a Q-learning-based variant …

Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning

2022-04-26 · Seungwoong Ha, Hawoong Jeong

How have individuals of social animals in nature evolved to learn from each other, and what would be the optimal strategy for such learning in a specific environment? Here, we address both problems by employing a deep re…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss

2020-01-15 · Pinkesh Badjatiya, Mausoom Sarkar, Abhishek Sinha, Siddharth Singh 외

In social dilemma situations, individual rationality leads to sub-optimal group outcomes. Several human engagements can be modeled as a sequential (multi-step) social dilemmas. However, in contrast to humans, Deep Reinfo…

Clustering Deep Reinforcement Learning Multi-agent Reinforcement Learning Reinforcement Learning

Social-Aware Incentive Mechanism for VehicularCrowdsensing by Deep Reinforcement Learning

2020-08-21 · IEEE Transactions on Intelligent Transportation Systems 2020 8 · Yinuo Zhao, Chi Harold Liu

Vehicular crowdsensing (VCS) takes the advantage of vehicles’ mobility and exploits both the crowd wisdom and sensing abilities offered by vehicle drivers’ carried smart mobile devices and on-board sensors to accomplish …

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning

2023-10-06 · Atsushi Ueshima, Shayegan Omidshafiei, Hirokazu Shirado

Cooperation is challenging in biological systems, human societies, and multi-agent systems in general. While a group can benefit when everyone cooperates, it is tempting for each agent to act selfishly instead. Prior hum…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning