Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiple model-based reinforcement learning
Neural Computation
Training products of experts by minimizing contrastive divergence
Neural Computation
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Hi-index | 0.00 |
In real-world problems, the environment surrounding a controlled system is nonstationary, and the optimal control may change with time. It is difficult to learn such controls when using reinforcement learning (RL) which usually assumes stationary Markov decision processes. A modular-based RL method was formerly proposed by Doya et al., in which multiple-paired predictors and controllers were gated to produce nonstationary controls, and its effectiveness in nonstationary problems was shown. However, learning of time-dependent decomposition of the constituent pairs could be unstable, and the resulting control was somehow obscure due to the heuristical combination of predictors and controllers. To overcome these difficulties, we propose a new modular RL algorithm, in which predictors are learned in a self-organized manner to realize stable decomposition and controllers are appropriately optimized by a policy gradient-based RL method. Computer simulations show that our method achieves faster and more stable learning than the previous one.