This paper surveys the eld of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the eld and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environme...

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...

this paper describes how multi-agent system technology can be used as the underpinning platform for voltage control in power systems. in this study, some facts (flexible ac transmission systems) devices are properly designed to coordinate their decisions and actions in order to provide a coordinated secondary voltage control mechanism based on multi-agent theory. each device here is modeled as ...

anti-lock braking system (abs) is a nonlinear and time varying system including uncertainty, so it cannot be controlled by classic methods. intelligent methods such as fuzzy controller are used in this area extensively; however traditional fuzzy controller using simple type-1 fuzzy sets may not be robust enough to overcome uncertainties. for this reason an interval type-2 fuzzy controller is de...

An emergence of intelligent behavior within a simple robotic agent is studied in this paper. Two control mechanisms for an agent are considered — new direction of reinforcement learning called relational reinforcement learning, and a radial basis function neural network trained by evolutionary algorithm. Relational reinforcement learning is a new interdisciplinary approach combining logical pro...

Reinforcement learning is one of the main adaptive mechanisms that is both well documented in animal behaviour and giving rise to computational studies in animats and robots. In this paper, we present TeXDYNA, an algorithm designed to solve large reinforcement learning problems with unknown structure by integrating hierarchical abstraction techniques of Hierarchical Reinforcement Learning and f...

Knowledge Representation is important issue in reinforcement learning. In this paper, we bridge the gap between reinforcement learning and knowledge representation, by providing a rich knowledge representation framework, based on normal logic programs with answer set semantics, that is capable of solving model-free reinforcement learning problems for more complex domains and exploits the domain...

Reinforcement learning is key research in automatic control, and hierarchical reinforcement learning is a good solution to the problem of the curse of dimensionality. Hierarchical reinforcement learning can only deal with discrete space, but the state and action spaces in robotic automatic control are continuous. In order to deal with continuous spaces in hierarchical reinforcement learning, we...

Previous approaches to multi agent reinforcement learning are either very limited or heuristic by na ture The main reason is each agent s environment continually changes because the other agents keep changing Traditional reinforcement learning algo rithms cannot properly deal with this This paper however introduces a novel general sound method for multiple reinforcement learning agents living a...

This thesis presents novel work on how to improve exploration in reinforcement learning using domain knowledge and knowledge-based approaches to reinforcement learning. It also identifies novel relationships between the algorithms’ and domains’ parameters and the exploration efficiency. The goal of solving reinforcement learning problems is to learn how to execute actions in order to maximise t...