27 research outputs found
Emotional Motion Generation Using Emotion Representation Rules Modeled for Human Affection
A Crossover Operation for Evolutionary Binary Decision Diagram to Acquire Humanoid Robot Motions
Subjective Effects of Face Change with Emotion Transition by Using Emotional Space of Communication Robot "ifbot"
Feasibility of Collaborative Learning with Robots Which Prompts Constructive Interaction
Obtaining Robot's Behavior to Avoid Danger by Using Probability Based Reinforcement Learning
A dynamic allocation method of basis functions in reinforcement learning
Abstract. In this paper, we propose a dynamic allocation method of basis functions, an Allocation/Elimination Gaussian Softmax Basis Function Network (AE-GSBFN), that is used in reinforcement learning. AE-GSBFN is a kind of actor-critic method that uses basis functions. This method can treat continuous high-dimensional state spaces, because basis functions required only for learning are dynamically allocated, and if an allocated basis function is identified as redundant, the function is eliminated. This method overcomes the curse of dimensionality and avoids a fall into local minima through the allocation and elimination processes. To confirm the effectiveness of our method, we used a maze task to compare our method with an existing method, which has only an allocation process. Moreover, as learning of continuous high-dimensional state spaces, our method was applied to motion control of a humanoid robot. We demonstrate that the AE-GSBFN is capable of providing better performance than the existing method