When a robot interacts with the environment producing changes through its own actions, it should find opportunities for learning and updating its own models of the environment. A robot that is able to construct discrete models of the underlying dynamical system which emerges from this interaction can guide its own behavior and adapt it based on feedback from the environment. Thus, the induction...

Reliable and effective multi-task learning is a prerequisite for the development of robotic agents that can quickly learn to accomplish related, everyday tasks. However, in the reinforcement learning domain, multi-task learning has not exhibited the same level of success as in other domains, such as computer vision. In addition, most reinforcement learning research on multitask learning has bee...

Developing a dialogue agent that is capable of making autonomous decisions and communicating by natural language is one of the long-term goals of machine learning research. Traditional approaches either rely on hand-crafting a small state-action set for applying reinforcement learning that is not scalable or constructing deterministic models for learning dialogue sentences that fail to capture ...

The paper presents an algorithm for an EM-based reinforcement-driven clustering. As shown here it is applicable to the reinforcement learning setting with continuous state/discrete action space. E-step of the algorithm computes the posterior given the data and the reinforcement. Although designed to discover intrinsic states, the algorithm performs action selection without explicit state identi...

Modeling the behavior of the dialogue management in the design of a spoken dialogue system using statistical methodologies is currently a growing research area. This paper presents a work on developing an adaptive learning approach to optimize dialogue strategy. At the core of our system is a method formalizing dialogue management as a sequential decision making under uncertainty whose underlyi...

In this paper, a novel reinforcement learning method inspired by the way humans learn from others is presented. This method is developed based on cellular learning automata featuring a modular design and cooperation techniques. The modular design brings flexibility, reusability and applicability in a wide range of problems to the method. This paper focuses on analyzing sensitivity of the method...

In the quest for machines that are able to learn, reinforcement learning (RL) is found to be an appealing learning methodology. A known problem in this learning method, however, is that it takes too long before the robot learns to associate suitable situation-action pairs. Due to this problem, RL has remained applicable only to simple tasks and discrete environment. To accelerate the learning p...

Reinforcement learning (RL) can obtain the supervisory controller for discrete-event systems modeled by finite automata and temporal logic. The published methods often have two limitations. First, a large number of training data are required to learn RL controller. Second, algorithms do not consider uncontrollable events, which essential control theory (SCT). To address limitations, we first ap...