arXiv:2601. 21523v2 Announce Type: replace Abstract: To promote cooperation in Multi-Agent Reinforcement Learning, the reward signals of all agents can be aggregated together, forming global rewards that are commonly known as the fully cooperative setting.
arXiv:2605. 17393v2 Announce Type: replace Abstract: Coordination graphs are a central abstraction in cooperative multi-agent reinforcement learning (MARL), yet existing sparse-graph learners lack a theoretically grounded mechanism to decide which edges should exist and how much information each edge should carry.
arXiv:2608. 20016v1 Announce Type: cross Abstract: Reputation is widely recognized as a key mechanism for sustaining cooperation.
arXiv:2609.06586v1 Announce Type: cross Abstract: A shared reward gives agents a common objective, but leaves open when, how and even whether they must cooperate to succeed. We address these question...
arXiv:2609.25701v1 Announce Type: new Abstract: We study distributed Byzantine-resilient actor-critic multi-agent reinforcement learning (AC-MARL), where agents collectively learn policies through lo...
The paper introduces Continual Graph Multi-Agent Reinforcement Learning (CGMARL), a framework that maps sequences of tasks into attributed graphs to capture task-specific structures such as network topologies or formation arrangements. It presents the Graph-based Formation (GRAFO) benchmark to illustrate forgetting in graph-based CMARL settings and proposes the Frozen Graph Encoder (FROG) to preserve past structural information using a frozen graph backbone. Experiments on GRAFO demonstrate that combining FROG with existing continual learning methods significantly improves performance across multiple CGMARL scenarios.
We’re releasing an algorithm which accounts for the fact that other agents are learning too, and discovers self-interested yet collaborative strategies like tit-for-tat in the iterated prisoner’s dilemma. This algorithm, Learning with Opponent-Learning Awareness (LOLA), is a small step towards agents that model other minds.
The paper presents a symmetric solution to the Bellman optimality equation for a repeated harmony game, revealing three distinct symmetric solutions. One solution corresponds to the trivial All‑Cooperate strategy, another aligns with the Win‑stay Lose‑shift strategy known from the prisoner's dilemma, and a third nontrivial strategy is explored in detail. The authors also numerically examine which of these strategies agents learn using a reinforcement learning algorithm.
The paper investigates whether evolutionary stability guarantees that learning agents can achieve cooperative outcomes in a multi‑agent setting. Using a three‑agent governance game, the authors compare the evolutionary basin of attraction with learning basins derived from independent Q‑learning, scaled Boltzmann exploration, and SA–EA BQL. They find that while the evolutionary basin covers the entire sampled grid, only ε‑greedy Q‑learning attains a substantial learning basin, whereas the other methods fail to sustain cooperation, highlighting a disconnect between population‑level stability and finite‑sample learning accessibility.
The paper introduces Preference-based Opponent Shaping (PBOS), a method that incorporates a preference parameter into an agent’s loss function to directly consider an opponent’s loss during strategy updates. By jointly learning strategy and preference parameters, PBOS aims to guide agents toward cooperative or competitive behaviors without relying on simple opponent predictions. Experiments on differentiable games demonstrate that PBOS enables agents to achieve better reward distributions across various environments.
arXiv:2606. 25073v1 Announce Type: new Abstract: In cooperative multi-agent reinforcement learning (MARL), from a deployment perspective, it is challenging and expensive to train agents from scratch for each new environment or task.
arXiv:2606. 04750v1 Announce Type: new Abstract: Instilling virtuous behavior in artificial intelligence has seen increasing interest.