arXiv Machine Learning
Jun 15

Contract-Based Compositional Shielding for Safe Multi-Agent Reinforcement Learning

arXiv:2606. 14130v1 Announce Type: new Abstract: Safe coordination problems surface in multi-agent reinforcement learning when global safety cannot be enforced by any agent unilaterally: the admissibility of one agent's action may depend on the dynamics of other agents.

By Omar Adalat, Edwin Hamel-De le Court, Francesco Belardinelli
arXiv AI
Aug 21

Adaptive Probabilistic Shielding by Learning MDPs for Safe Reinforcement Learning

arXiv:2608. 19836v1 Announce Type: cross Abstract: Probabilistic shielding is a technique for safe reinforcement learning (RL).

By Astrid Horn Brorholt (Aalborg University, Aalborg, Denmark), Maris F. L. Galesloot (Radboud University, Nijmegen, Netherlands), Nils Jansen (Radboud University, Nijmegen, Netherlands), Kim Guldstrand Larsen (Aalborg University, Aalborg, Denmark), Christian Schilling (Aalborg University, Aalborg, Denmark)
arXiv Machine Learning
Aug 6

Communication-Enhanced Tutoring for Efficient Decentralized Multi-Agent Reinforcement Learning

arXiv:2508. 13661v4 Announce Type: replace Abstract: Centralized Training with Decentralized Execution (CTDE) is the dominant paradigm in multi-agent reinforcement learning (MARL), enabling agents to act independently at test time while leveraging additional information during training.

By Maciej Wojtala, Bogusz Stefa\'nczyk, Dominik Bogucki, {\L}ukasz Lepak, Pawe{\l} Wawrzy\'nski