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...

abstract: ِintroduction: the effect of morphine dependency on learning and spatial memory is controversial. so in this study effect of co-administeration of nitric oxide (no) and morphine in ca3 of hippocampus on learning and spatial memory in morphine dependent rats was investigated. methods: after anaesthetization of male rats, cannulae implanted bilaterally in ca3 of hippocampus. after recove...

The purpose of reinforcement learning is to maximize rewards from environment, and reinforcement learning agents learn by interacting with external environment through trial and error. Q-Learning, a representative reinforcement learning algorithm, is a type of TD-learning that exploits difference in suitability according to the change of time in learning. The method obtains the optimal policy t...

Background It is often said that children with Fetal Alcohol Spectrum Disorder (FASD) have difficulty learning from reinforcement. However, there is little empirical evidence to support or deny this claim. Objectives To examine reinforcement learning in children with FASD, specifically: (1) the rate of learning from reinforcement; and (2) the impact of concreteness of the reinforcer. Methods Pa...

This paper proposes a TD (temporal difference) and GA (genetic algorithm) based reinforcement (TDGAR) neural learning scheme for controlling chaotic dynamical systems based on the technique of small perturbations. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to fulfill the reinforcement learning task. Structurely, the TDGAR learning system i...

Intelligent systems is a major research theme in Nanjing University, with the support from the State Key Laboratory for Novel Software Technology of China, one of the top laboratories in the information technology field in the whole country. Currently, the research carried out by the intelligent systems group at Nanjing University mainly fo-cuses on the following topics: • Fundamental methods o...

In the last few years, reinforcement learning algorithms have been proposed as a more natural way of modelling animal learning. Unlike supervised learning methods, reinforcement learning addresses the basic problem faced by an animal when trying to control a discrete stochastic dynamic system: discover by trial and error a policy of actions that maximises some criterium of optimality, usually e...

As an important method to solve sequential decisionmaking problems, reinforcement learning learns the policy of tasks through the interaction with environment. But it has difficulties scaling to largescale problems. One of the reasons is the exploration and exploitation dilemma which may lead to inefficient learning. We present an approach that addresses this shortcoming by introducing qualitat...

The robot soccer system is being used as a test bed to develop the next generation of field robots. In the multiagent system, action selection is important for the cooperation and coordination among agents. There are many techniques in choosing a proper action of the agent. As the environment is dynamic, reinforcement learning is more suitable than supervised learning. Reinforcement learning is...

The rise of social networking enables the development multilingual Internet-accessible digital documents in several languages. document needs to be evaluated physically through Cross-Language Text Summarization (CLTS) involved disparate and generation source documents. Cross-language processing is from language sources toward targeted need processed with contextual semantic data decoding scheme...