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cs.LG2026
Learning to maintain safety through expert demonstrations in settings with unknown constraints: A Q-learning perspective
George Papadopoulos, George A. Vouros
Given a set of trajectories demonstrating the execution of a task safely in a constrained MDP with observable rewards but with unknown constraints and non-observable costs, we aim…
cs.LG2025
Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration
Andreas Kontogiannis, Konstantinos Papathanasiou, Yi Shen +3
Learning to cooperate in distributed partially observable environments with no communication abilities poses significant challenges for multi-agent deep reinforcement learning (MAR…
cs.LG2025
An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Tasks
George Papadopoulos, Andreas Kontogiannis, Foteini Papadopoulou +3
Multi-Agent Reinforcement Learning (MARL) has recently emerged as a significant area of research. However, MARL evaluation often lacks systematic diversity, hindering a comprehensi…