In a standard view of the reinforcement learning problem, an agent's goal is to efficiently
identify a policy that maximizes long-term reward. However, this perspective is based on a …

The reward hypothesis posits that," all of what we mean by goals and purposes can be well
thought of as maximization of the expected value of the cumulative sum of a received scalar …

Reinforcement Learning (RL) algorithms typically utilize learning and/or planning
techniques to derive effective policies. The integration of both approaches has proven to be …

Abstract Learning from Preferential Feedback (LfPF) plays an essential role in training Large
Language Models, as well as certain types of interactive learning agents. However, a …

Decision-making in large and dynamic environments has always been a challenge for AI
agents. Given the multitude of available sensors in robotics and the rising complexity of …

Reward binarisation is a common heuristically applied technique which can potentially
simplify a given reinforcement learning problem. However this procedure done without care …