Obtaining Humanoid Robot Controller Using Reinforcement Learning
Masayoshi Kanoh, Hidenori Itoh · 2007
In this chapter, we proposed a dynamic allocation method of basis functions, AE-GSBFN, in reinforcement learning. Through allocation and elimination processes, AE-GSBFN overcomes the curse of dimensionality and avoids a fall into local minima. To confirm the effectiveness of AE-GSBFN, we applied it to the motion control of a humanoid robot. We demonstrated that AE-GSBFN is capable of providing better performance than A-GSBFN, and we succeeded in enabling the learning of motion control of the robot. The future objective of this study is to do some general comparisons of our method with other dynamic neural networks, for example, Fritzke's "Growing Neural Gas" (Fritzke, 1996) and Marsland's "Grow When Required Nets" (Marsland et al., 2002). An analysis of the necessity of hierarchical reinforcement learning methods proposed by Morimoto and Doya (Morimoto & Doya, 2000) in relation to the standing up simulation is also an important issue for the future study.