3 accepted papers
Offline Safe Reinforcement Learning (OSRL) aims to learn a policy that achieves high performance in sequential decision-making while satisfying safety constraints, using only pre-collected datasets. Recent works, inspired by the strong capabilities of Generative Models (GMs), reformulate decision-ma…
Reinforcement learning (RL) has proven to be well-performed and versatile in inventory control (IC). However, further improvement of RL algorithms in the IC domain is impeded by two limitations of online experience. First, online experience is expensive to acquire in real-world applications. With th…
In multi-agent reinforcement learning (MARL), effective exploration is critical, especially in sparse reward environments. Although introducing global intrinsic rewards can foster exploration in such settings, it often complicates credit assignment among agents. To address this difficulty, we propos…