Learning policy representations in multiagent systems

Multi-Goal Reinforcement Learning: Challenging robotics environments and request for research

Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches

arXiv:2606. 31347v1 Announce Type: new Abstract: The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as increased peak demand, voltage fluctuations, line overloads, and the integration of variable renewable energy sources.

Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning

arXiv:2507. 23604v2 Announce Type: replace Abstract: Decentralized Multi-Agent Reinforcement Learning (MARL) methods allow for learning scalable multi-agent policies, but suffer from partial observability and induced non-stationarity.

Improving Generalization and Data Efficiency with Diffusion in Offline Multi-agent RL

arXiv:2307. 01472v2 Announce Type: replace Abstract: We present a novel Diffusion Offline Multi-agent Model (DOM2) for offline Multi-Agent Reinforcement Learning (MARL).

Multi-Agent Reinforcement Learning via Agent-Specific Preference

arXiv:2608. 08604v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) is a powerful framework for solving complex collaborative tasks, but it relies heavily on well-defined global reward functions.

Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes

The paper investigates how human observers interpret the learning processes of reinforcement learning (RL) agents. Using a novel observation-based paradigm, the authors conducted two experiments: an exploratory interview study with nine participants that identified four core themes—Agent Goals, Knowledge, Decision Making, and Learning Mechanisms—and a confirmatory study with 34 participants that applied the paradigm across navigation and manipulation tasks and two RL algorithms. Analyses of 816 responses validated the paradigm’s reliability and refined the thematic framework, showing how these themes evolve over time and interrelate.