Reinforcement schemes represent the basis of the learning process for stochastic learning automata, generating their learning behavior. An automaton using a reinforcement scheme can decide the best action, based on past actions and environment responses. The aim of this paper is to introduce a new reinforcement scheme for stochastic learning automata. We test our schema and compare with other n...

Trust management and Reputation models are becoming integral part of Internet based applications such as CSCW, E-commerce and Grid Computing. Also the trust dimension is a significant social structure and key to social relations within a collaborative community. Collaborative Decision Making (CDM) is a difficult task in the context of distributed environment (information across different geogra...

Reinforcement learning has its origin from the animal learning theory. RL does not require prior knowledge but can autonomously get optional policy with the help of knowledge obtained by trial-and-error and continuously interacting with the dynamic environment. Due to its characteristics of self improving and online learning, reinforcement learning has become one of intelligent agent’s core tec...

Recent advancements in reinforcement learning confirm that reinforcement learning techniques can solve large scale problems leading to high quality autonomous decision making. It is a matter of time until we will see large scale applications of reinforcement learning in various sectors, such as healthcare and cyber-security, among others. However, reinforcement learning can be time-consuming be...

Reinforcement learning is a very general unsupervised learning mechanism. Due to its generality reinforcement learning does not scale very well for tasks that involve inferring subtasks. In particular when the subtasks are dynamically changing and the environment is adversarial. One of the most challenging reinforcement learning tasks so far has been the 3 to 2 keepaway task in the RoboCup simu...

by MARGARET MARY SKELLY This dissertation investigates the incorporation of function approximation and hierarchy into reinforcement learning for use in an adaptive control setting through empirical studies. Reinforcement learning is an artificial intelligence technique whereby an agent discovers which actions lead to optimal task performance through interaction with its environment. Although re...

Hierarchical reinforcement learning is an increasingly popular research field. In hierarchical reinforcement learning the complete learning task is decomposed into smaller subtasks that are combined in a hierarchical network. The subtasks can then be learned independently. A hierarchical decomposition can potentially facilitate state abstractions (i.e., bring forth a reduction in state space co...

Reinforcement and error-based processes are essential for motor learning, with the cerebellum thought to be required only for the error-based mechanism. Here we examined learning and retention of a reaching skill under both processes. Control subjects learned similarly from reinforcement and error-based feedback, but showed much better retention under reinforcement. To apply reinforcement to ce...

OF DISSERTATION AN ECHO STATE MODEL OF NON-MARKOVIAN REINFORCEMENT LEARNING There exists a growing need for intelligent, autonomous control strategies that operate in real-world domains. Theoretically the state-action space must exhibit the Markov property in order for reinforcement learning to be applicable. Empirical evidence, however, suggests that reinforcement learning also applies to doma...