Reinforcement learning with Marr

Reinforcement learning with Marr.

To many, the poster child for David Marr's famous three levels of scientific inquiry is reinforcement learning-a computational theory of reward optimization, which readily prescribes algorithmic solutions that evidence striking resemblance to signals found in the brain, suggesting a straightforward neural implementation. Here we review questions that remain open at each level of analysis, concl...

Reinforcement Learning with Time

This paper steps back from the standard infinite horizon formulation of reinforcement learning problems to consider the simpler case of finite horizon problems. Although finite horizon problems may be solved using infinite horizon learning algorithms by recasting the problem as an infinite horizon problem over a state space extended to include time, we show that such an application of infinite ...

Reinforcement Learning with Preferences

In this work, we propose a framework of learning with preferences, which combines some neurophysiological findings, prospect theory, and the classic reinforcement learning mechanism. Specifically, we extend the state representation of reinforcement learning with a multi-dimensional preference model controlled by an external state. This external state is designed to be independent from the reinf...

Clustering with Reinforcement Learning

We show how a previously derived method of using reinforcement learning for supervised clustering of a data set can lead to a sub-optimal solution if the cluster prototypes are initialised to poor positions. We then develop three novel reward functions which show great promise in overcoming poor initialization. We illustrate the results on several data sets. We then use the clustering methods w...

Reinforcement Learning with Exploration