In humans and animals, reward prediction error encoded by dopamine systems is thought to be important in the temporal difference learning class of reinforcement learning (RL). With RL algorithms, many brain models have described the function of dopamine and related areas, including the basal ganglia and frontal cortex. In spite of this importance, how the reward prediction error itself is compu...

As a way of resolving vehicle congestion, there is a feedback control approach which models a traffic network as a discrete dynamical system and derives feedback gain for controlling green light times of each junction. Since the input is the sensory observed traffic flow of each link, and since the state equation models both the topology and the parameters of the network, it is effective for ad...

Markov games are a generalization of Markov decision process to a multi-agent setting. Two-player zero-sum Markov game framework offers an effective platform for designing robust controllers. This paper presents two novel controller design algorithms that use ideas from game-theory literature to produce reliable controllers that are able to maintain performance in presence of noise and paramete...

This paper compares direct reinforcement learning (no explicit model) and model-based reinforcement learning on a simple task: pendulum swing up. We nd that in this task model-based approaches support reinforcement learning from smaller amounts of training data and eecient handling of changing goals.

This report surveys recent development on the global combinatorial optimization using reinforcement learning methods. It introduces the general background of combinatorial optimization problems and reinforcement learning techniques, describes observations and previous works in this area, and focuses on Boyan and Moore's recent work, the STAGE algorithm with the assistant of reinforcement learning.

Reinforcement learning has received much attention in the past decade. The primary thrust of this research has focused on tabula rasa learning methods. That is, the learning agent is initially unaware of its environment and must learn or re-learn everything. We feel that this is neither realistic nor effective. While the agent may start out with little or no knowledge of its environment, it mus...

BACKGROUND Negative reinforcement results in behavior to escape or avoid an aversive outcome. Withdrawal symptoms are purported to be negative reinforcers in perpetuating substance dependence, but little is known about negative reinforcement learning in this population. The purpose of this study was to examine reinforcement learning in substance dependent individuals (SDI), with an emphasis on ...

This paper focuses on a systematic treatment for developing a generic online learning control system based on the fundamental principle of reinforcement learning or more specifically neural dynamic programming. This online learning system improves its performance over time in two aspects: 1) it learns from its own mistakes through the reinforcement signal from the external environment and tries...

Exploration plays a fundamental role in any active learning system. This study evaluates the role of exploration in active learning and describes several local techniques for exploration in nite, discrete domains, embedded in a reinforcement learning framework (delayed reinforcement). This paper distinguishes between two families of exploration schemes: undirected and directed exploration. Whil...

Many Intelligent Tutoring Systems have been developed using different Artificial Intelligence techniques. In this paper we propose to use Reinforcement Learning for building an intelligent tutoring system to teach autistic students, who can't communicate well with others. In reinforcement learning, a policy is updated for taking appropriate action to teach the student. The main advantage of usi...