A novel integrative learning architecture, RLSM with a STDP network is described. This architecture models symbol emergence in an autonomous agent engaged in reinforcement learning tasks. The architecture consists of two constitutional learning architectures: a reinforcement learning schema model (RLSM) and a spike timing-dependent plasticity (STDP) network. RLSM is an incremental modular reinf...

This paper presents a direct reinforcement learning algorithm, called Finite-Element Reinforcement Learning, in the continuous case, i.e. continuous state-space and time. The evaluation of the value function enables the generation of an optimal policy for reinforcement control problems, such as target or obstacle problems, viability problems or optimization problems. We propose a continuous for...

Recently, the interest in reinforcement learning in game playing has been renewed. This is evidenced by the groundbreaking results achieved by AlphaGo. General Game Playing (GGP) provides a good testbed for reinforcement learning, currently one of the hottest fields of AI. In GGP, a specification of games rules is given. The description specifies a reinforcement learning problem, leaving progra...

The \forward modeling" approach of Jor-dan and Rumelhart has been shown to be applicable when supervised learning methods are to be used for solving reinforcement learning tasks. Because such tasks are natural candidates for the application of reinforcement learning methods, there is a need to evaluate the relative merits of these two learning methods on reinforcement learning tasks. We present...

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...

ion in reinforcement learning. Artificial Intelligence, 112(1-2): 181–211. URBANOWICZ, R. J., and J. H. MOORE. 2009. Learning classifier systems: A complete introduction, review, and roadmap. Journal of Artificial Evolution and Applications, 2009. doi: 10.1155/2009/736398. WATKINS, C. J. 1989. Learning from delayed rewards. Ph.D. thesis, Cambridge University, Cambridge, UK. WHITE, S., T. R. MAR...

In recent years, attention has been focused on the relationship between black box optimization and reinforcement learning. Black box optimization is a framework for the problem of finding the input that optimizes the output represented by an unknown function. Reinforcement learning, by contrast, is a framework for finding a policy to optimize the expected cumulative reward from trial and error....

In this lab you will learn about dynamic programming and reinforcement learning. It is assumed that you are familiar with the basic concepts of reinforcement learning and that you have read chapter 13 in the course book Machine Learning (Mitchell, 1997). The first four chapters of the survey on reinforcement learning by Kaelbling et al. (1996) is a good supplementary material. For further readi...