Motor Learning Model through Reinforcement Learning with Neural Internal Model

Structure Learning in Motor Control: A Deep Reinforcement Learning Model

Motor adaptation displays a structure-learning effect: adaptation to a new perturbation occurs more quickly when the subject has prior exposure to perturbations with related structure. Although this ‘learning-to-learn’ effect is well documented, its underlying computational mechanisms are poorly understood. We present a new model of motor structure learning, approaching it from the point of vie...

Efficient Learning Through Evolution: Neural Programming and Internal Reinforcement

A neural model of hierarchical reinforcement learning

We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm o...

Reinforcement Learning in Neural Networks: A Survey

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

Reinforcement Learning through Neural Encoding

Recent progress in the field of Reinforcement Learning (RL) has enabled to tackle bigger and more challenging tasks. However, the increasing complexity of the problems, as well as the use of more sophisticated models such as Deep Neural Networks (DNN), has impeded the ability to understand the behavior of trained policies. In this work, we present the Semi-Aggregated Markov Decision Process (SA...