3 papers
cs.LG2025
Model-Based Exploration in Monitored Markov Decision Processes
Alireza Kazemipour, Simone Parisi, Matthew E. Taylor +1
A tenet of reinforcement learning is that the agent always observes rewards. However, this is not true in many realistic settings, e.g., a human observer may not always be availabl…
cs.AI2024
A Novel Framework for Automated Warehouse Layout Generation
Atefeh Shahroudnejad, Payam Mousavi, Oleksii Perepelytsia +4
Optimizing warehouse layouts is crucial due to its significant impact on efficiency and productivity. We present an AI-driven framework for automated warehouse layout generation. T…
cs.LG2024
Monitored Markov Decision Processes
Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour +2
In reinforcement learning (RL), an agent learns to perform a task by interacting with an environment and receiving feedback (a numerical reward) for its actions. However, the assum…