Multi-Agent Reinforcement Learning
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MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment
Announce Type: replace Abstract: Offline Multi-agent Reinforcement Learning (MARL) is valuable in scenarios where online interaction is impractical or risky. While independent learning in MARL offers flexibility and scalability, accurately assigning credit to individual agents in offline settings poses challenges because interactions with an environment are prohibited. In this paper, we propose a new framework, namely Multi-Agent Causal Credit Assignment (MACCA), to address credit assignment...
Enhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling
arXiv:2606.05021v1 Announce Type: new Abstract: We investigate multi-agent deep reinforcement learning and propose two enhancements to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. First, we introduce a novel Action Inference mechanism that enables each agent to predict other agents' intended actions, thereby improving the accuracy and stability of its own policy. Second, we apply an importance sampling strategy, using geometric distribution, in the replay buffer to...
GARL: Game-Theoretic Reinforcement Learning for Multi-Agent Strategic Prioritisation
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Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters
arXiv:2606.02107v1 Announce Type: new Abstract: This paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework for quadcopter consensus control. Compared to conventional multi-agent MARL formulations that rely on centralized planning or fully decentralized execution, ND-MARL incorporates the swarm communication graph into the decision process. Under a 2-Neighbor communication topology, each agent observes information of only two neighbors and outputs an...
Dreaming Of Others: Latent Teammate Modeling In World Models For Multi-Agent Reinforcement Learning
Announce Type: new Abstract: In cooperative multi-agent reinforcement learning (MARL), agents must coordinate with partners whose internal policies and intentions are not directly observable. While world models such as Dreamer have demonstrated strong generalization and sample efficiency in single-agent settings, their application to MARL remains limited by an inability to handle teammate-induced uncertainty. We propose a new perspective: treat teammates as structured, learnable components...
Generalized Intention Modeling in Multi-Agent Reinforcement Learning
arXiv:2605.31318v1 Announce Type: new Abstract: Modeling an opponent's intent is critical for effective decision-making in non-cooperative, competitive, and general-sum multi-agent reinforcement learning. Existing opponent modeling methods encode intent using an embedding derived from episode information chosen a priori, such as the opponent's next action or a future environment state, and use this to guide the ego-agent's behavior.
LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning
arXiv:2605.18077v2 Announce Type: replace Abstract: Communication is a key component in multi-agent reinforcement learning (MARL) for mitigating partial observability, yet prior approaches often rely on inefficient information exchange or fail to transmit sufficient state information. To address this, we propose LLM-driven Multi-Agent Communication (LMAC), which leverages an LLM's reasoning capability to design a communication protocol that enables all agents to reconstruct the underlying...
Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics
Announce Type: new Abstract: We present a distributed approach for constrained Multi-Agent Reinforcement Learning (MARL) that combines state-augmented policy learning with distributed consensus over dual variables. Our method targets systems where agents have separable dynamics but must coordinate to satisfy global resource constraints, a setting in which, as we demonstrate empirically, independent learning fails to produce feasible solutions because agents cannot determine appropriate...
Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning
Announce Type: replace Abstract: Multi-agent reinforcement learning (MARL) in real-world use cases may need to adapt to external natural language instructions that interrupt ongoing behavior and conflict with long-horizon objectives. However, conditioning rewards on instructions introduces a fundamental failure mode as Bellman updates couple value estimates across instruction contexts, leading to inconsistent values when instructions interrupt macro-actions. We propose Macro-Action Value...
MARFT: Multi-Agent Reinforcement Fine-Tuning
Announce Type: replace Abstract: Large Language Model (LLM)-based Multi-Agent Systems (LaMAS) have demonstrated strong capabilities on complex agentic tasks requiring multifaceted reasoning and collaboration, from high-quality presentation generation to scientific research. Meanwhile, Reinforcement Learning (RL) is widely recognized for enhancing agent intelligence, but limited work has studied fine-tuning LaMAS with foundational RL techniques. Directly applying conventional Multi-Agent...