Learning to behave: an investigation of connectionist approaches to behaviour-based control in autonomous agents

Most reinforcement learning applied to autonomous agents has relied on a coarse discretization of the control space. This paper presents a modular connectionist architecture for the autonomous control of a mobile agent based on a form of continuous reinforcement learning using backpropagation through random number generators. It discusses the potential of this approach as a way of decomposing a complex goal so that the structural credit assignment problem is made tractable and the complexity of the neural network topology necessary for solving a problem is reduced.

[1]  R. J. Williams,et al.  On the use of backpropagation in associative reinforcement learning , 1988, IEEE 1988 International Conference on Neural Networks.

[2]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[3]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[4]  George A. Bekey,et al.  A reinforcement-learning approach to reactive control policy design for autonomous robots , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.