3 accepted papers
Safe Reinforcement Learning (Safe RL) is one of the prevalently studied subcategories of trial-and-error-based methods with the intention to be deployed on real-world systems. In safe RL, the goal is to maximize reward performance while minimizing constraints, often achieved by setting bounds on con…
Safe reinforcement learning (Safe RL) refers to a class of techniques that aim to prevent RL algorithms from violating constraints in the process of decision-making and exploration during trial and error. In this paper, a novel model-free Safe RL algorithm, formulated based on the multi-objective po…
Multi-agent pathfinding plays a crucial role in various robot applications. Recently, deep reinforcement learning methods have been adopted to solve large-scale planning problems in a decentralized manner. Nonetheless, such approaches pose challenges such as non-stationarity and partial observabilit…