Motor kontrol ve beynin bilişsel karar verme mekanizmalarını analiz etmek üzere tersine pekiştirmeli öğrenme ile keşifler

Reinforcement Learning is a framework for generating optimal policies given a task and a reward/punishment structure. Likewise, Inverse Reinforcement Learning, as the name suggests, is used for recovering the reasoning behind an optimal policy based on demonstrations from an expert. We set out to explore whether recent Reinforcement Learning and Inverse Reinforcement Learning methods can serve as a computational tool for investigating optimality principles of motor control and cognitive decision-making mechanisms of the brain. For this purpose, we have targeted several different tasks involved with different parts of the sensorimotor learning mechanism of the brain. We aim to recover the optimality principles employed by the brain for various control and decision-making tasks. If this is achieved, we can analyze, understand, mimic and improve demonstrated behavior with less bias, which we hope is a step forward in understanding the process of learning in both human-based and artificial systems. For the scope of this thesis, we have evaluated two tasks. The first task was investigating the applicability of perceptual development for Reinforcement Learning. For this task, we have proposed a perceptual development based learning regime for a Reinforcement Learning agent, and the results obtained suggest that a suitable perceptual development regime may improve the learning progress and yield better-performing agents. The second task was to predict reward function parameters of a provided trajectory in a standing up under perturbation scenario. For this task, we have proposed two different Inverse Reinforcement Learning approaches. Our results indicate that we were able to infer valid reward parameters on synthetic data.