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...

See the abstract for Chapter C3. Delayed reinforcement learning (RL) concerns the solution of stochastic optimal control problems. In this section we formulate and discuss the basics of such problems. Solution methods for delayed RL will be presented in Sections C3.4 and C3.5. In these three sections we will mainly consider problems in which C3.4, C3.5 the state and control spaces are finite se...

In this paper, we address an under-represented class of learning algorithms in the study of connectionism: reinforcement learning. We first introduce these classic methods in a new formalism which highlights the particularities of implementations such as Q-Learning, QLearning with Hamming distance, Q-Learning with statistical clustering and Dyna-Q. We then present in this formalism a neural imp...

In reinforcement learning for multi-step problems, the sparse nature of the feedback aggravates the difficulty of learning to perform. This paper explores the use of a reinforcement learning architecture, leading to a discussion of reinforcement learning in terms of feature abstraction, credit-assignment, and temporal-difference learning. Issues discussed include: the conditioning of the reinfo...

Reinforcement learning is an approach for learning optimal action policy via experiencing, i.e. using observed reward in environment states. Reinforcement learning algorithms include adaptive dynamic programming, temporal difference learning and Q-learning[1]. Examples of successful applications of reinforcement learning are controller for sustained inverted flight on an autonomous helicopter [...

The goal of research was the effect of electronical learning media on the reinforcement of youth social behavior from the point of view of computer course professors and students of Islamic Azad University of Sari. The statistical population was included of all computer students and professors of I.A.U of Sari. The statistical sample was identified by using of the sample content identification ...

In this paper, we discuss situations arising with reinforcement learning algorithms, when the reinforcement is delayed. The decision to consider delayed reinforcement is typical in many applications, and we discuss some motivations for it. Then, we summarize Q-Learning, a popular algorithm to deal with delayed reinforcement, and its recent extensions to use it to learn fuzzy logic structures (F...

This paper proposed a robot reinforcement learning method based on learning classifier system. A Learning Classifier System is a accuracy-based machine learning system with gradient descent that combines reinforcement learning and rule discovery system. The genetic algorithm and the covering operator act as innovation discovery components which are responsible for discovering new better reinfor...