To fully understand the properties of Accuracy-based Learning Classifier Systems, we need a formal framework that captures all components of classifier systems, that is, function approximation, reinforcement learning, and classifier replacement, and permits the modelling of them separately and in their interaction. In this paper we extend our previous work on function approximation [22] to rein...

Revealing reinforcing mechanisms in associative learning is important for elucidation of brain mechanisms of behavior. In mammals, dopamine neurons are thought to mediate both appetitive and aversive reinforcement signals. Studies using transgenic fruit-flies suggested that dopamine neurons mediate both appetitive and aversive reinforcements, through the Dop1 dopamine receptor, but our studies ...

We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples. Our framework is motivated by actor-critic reinforcement learning, but can be applied to both reinforcement and supervised learning. The key idea is to learn a meta-critic: an action-value function neural network that learns to criticise any actor trying to solve any specified task. For sup...

This chapter presents application of reinforcement learning to drug dosing personalization in treatment of chronic conditions. Reinforcement learning is a machine learning paradigm that mimics the trial-and-error skill acquisition typical for humans and animals. In treatment of chronic illnesses, finding the optimal dose amount for an individual is also a process that is usually based on trial-...

Batch reinforcement learning is a subfield of dynamic programming-based reinforcement learning. Originally defined as the task of learning the best possible policy from a fixed set of a priori-known transition samples, the (batch) algorithms developed in this field can be easily adapted to the classical online case, where the agent interacts with the environment while learning. Due to the effic...

Recurrent neural networks are often used for learning time-series data. Based on a few assumptions we model this learning task as a minimization problem of a nonlinear least-squares cost function. The special structure of the cost function allows us to build a connection to reinforcement learning. We exploit this connection and derive a convergent, policy iteration-based algorithm. Furthermore,...

This paper describes a method which senses changing environment by collecting failed instances, uses concept learning for acquiring a precondition for a control policy, and modifies the policy partially in reinforcement learning. The precondition of a policy represents the condition for reaching goals using the policy. Our method learns the precondition of a policy from the instances of policy ...

The use of reinforcement and rewards is known to enhance memory retention. However, the impact of reinforcement on higher-order forms of memory processing, such as integration and generalization, has not been directly manipulated in previous studies. Furthermore, there is evidence that sleep enhances the integration and generalization of memory, but these studies have only used reinforcement le...