Instance-Based Reinforcement Learning

Synonyms

Kernel-based reinforcement learning

Definition

Traditional reinforcement-learning (RL) algorithms operate on domains with discrete state spaces. They typically represent the value function in a table, indexed by states, or by state–action pairs. However, when applying RL to domains with continuous state, a tabular representation is no longer possible. In these cases, a common approach is to represent the value function by storing the values of a small set of states (or state–action pairs), and interpolating these values to other, unstored, states (or state–action pairs). This approach is known as instance-based reinforcement learning (IBRL). The instances are the explicitly stored values, and the interpolation is typically done using well-known instance-based supervised learning algorithms.

Motivation and Background

Instance-Based Reinforcement Learning (IBRL) is one of a set of value-function approximation techniques that allow standard RL algorithms to deal with problems...