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...

Reinforcement learning is very effec#ive for robot learning. Because it does not need prior knowledge and has higher capability of reactive and adaptive behaviors. In our previous works, we proposed new reinforce learning algorithm: "Q-learning with Dynamic Structuring of Exploration Space Based on Genetic Algorithm (QDSEGA)". It is designed for complicated systems with large action-state space...

While the need for hierarchies within control systems is apparent, it is also clear to many researchers that such hierarchies should be learned. Learning both the structure and the component behaviors is a diicult task. The beneet of learning the hierarchical structures of behaviors is that the decomposition of the control structure into smaller transportable chunks allows previously learned kn...

Reinforcement learning (RL) is a powerful machine-learning methodology that has an established theoretical foundation and has proven effective in a variety of small, simulated domains. There has been considerable work on applying RL, a method originally conceived for discrete state-action spaces, to problems with continuous states. The extension of RL to allow continuous actions, on the other h...

Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the environment it is operating in changes. This ability to learn in an unsupervised manner in a changing environment is applicable in complex domains through the use of function approximation of the domain’s policy. The function...