This paper discusses why traditional reinforcement learning methods, and algorithms applied to those models, result in poor performance in situated domains characterized by multiple goals, noisy state, and inconsistent reinforcement. We propose a methodology for designing reinforcement functions

Schultz, Wolfram. Predictive reward signal of dopamine neurons. is called rewards, which elicit and reinforce approach behav-J. Neurophysiol. 80: 1–27, 1998. The effects of lesions, receptor ior. The functions of rewards were developed further during blocking, electrical self-stimulation, and drugs

This paper investigates the problem of automatically learning how to restructure the reward function of a Markov decision process so as to speed up reinforcement learning. We begin by describing a method that learns a shaped reward function given a set of state and temporal abstractions. Next, we

Abstract. This paper addresses the problem of defining, from data, a reward func-tion in a Reinforcement Learning (RL) problem. This issue is applied to the case of Spoken Dialogue Systems (SDS), which are interfaces enabling users to interact in natural language. A new methodology which, from

A meta-analysis of 128 studies examined the effects of extrinsic rewards on intrinsic motivation. As predicted, engagement-contingent, completion-contingent, and performance-contingent rewards signifi-cantly undermined free-choice intrinsic motivation (d =-0.40,-0.36, and-0.28, respectively

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Existing inverse reinforcement learning (IRL) algorithms have assumed each ex-pert’s demonstrated trajectory to be produced by only a single reward function. This paper presents a novel generalization of the IRL problem that allows each trajectory to be generated by multiple locally consistent

Specifying the reward function of a Markov decision process (MDP) can be demanding, requiring human assessment of the precise quality of, and tradeoffs among, various states and actions. However, reward functions often possess considerable structure which can be leveraged to streamline

The purpose of this paper is to propose a fuzzy reward function for improving the learning efficiency of reinforcement learning. Reinforcement learning is a sort of on line learning approach. During the learning process, learning system (often called an agent) learns how to operate

or plans. Planning problems commonly possess structure in the reward and value functions used to de...