Roots of Reinforcement Learning
When we explore the roots of reinforcement learning, many of us come across the contributions of Richard S Sutton, a famous computer scientist. It is interesting to note that Richard Sutton was inspired by the pioneering works of American Researcher, Harry Klopf. Hedonistic Neuron is a seminal work by Harry Klopf, published in 1982. He has influenced the world of reinforcement learning like none has ever imagined. In this book, Klopf defines the heterostatic properties of neural networks. In an attempt to characterise consciousness, Klopf relates the construct to wave phenomena and suggests the further general equivalences of pleasure with “entropic processes”, and pain with “anti-entropic processes”.
By contrast with homeostatic systems seeking to maintain homeostasis, neurons and the systems they compose are envisaged as “heterostats”, which seek to achieve “heterostasis”. The concept of heterostasis can be considered at the root of artificial adaptive intelligence. A “homeostat” seeks to maintain a steady state whilst a “heterostatic” system seeks to achieve a better or optimal state. This book delves into the Hebbian concept of plasticity and self organisation. Klopf introduces another hypothesis that the capacity of the Limbic System and Hypothalumus to distinguish between self and other is “severely limited”.
Reinforcement learning is a significant invention in the world of machine learning and artificial intelligence. Reinforcement learning aims to solve problems that involve sequential optimal decisions under uncertainty…