Reinforcement Learning Models and Algorithms for Diabetes Management

Algorithms for Reinforcement Learning

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner’s predictions. Further, the predictions may have long term effects through influ...

Evolutionary Algorithms for Reinforcement Learning

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal di erence methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal di erence methods. This article focuses on the application of evo...

Algorithms for Inverse Reinforcement Learning

Reinforcement Learning Algorithms for MDPs

This article presents a survey of reinforcement learning algorithms for Markov Decision Processes (MDP). In the first half of the article, the problem of value estimation is considered. Here we start by describing the idea of bootstrapping and temporal difference learning. Next, we compare incremental and batch algorithmic variants and discuss the impact of the choice of the function approximat...

Algorithms and Representations for Reinforcement Learning

Machine Learning is a field of research aimed at constructing intelligent machines that gain and improve their skills by learning and adaptation. As such, Machine Learning research addresses several classes of learning problems, including for instance, supervised and unsupervised learning. Arguably, the most ubiquitous and realistic class of learning problems, faced by both living creatures and...