Generalised Task Planning with First-Order Function Approximation

Abstract

Real world robotics often operates in uncertain and dynamic environments where generalisation over different scenarios is of practical interest. In the absence of a model, value-based reinforcement learning can be used to learn a goal-directed policy. Typically, the interaction between robots and the objects in the environment exhibit a first-order structure. We introduce first-order, or relational, features to represent an approximation of the Q-function so that it can induce a generalised policy. Empirical results for a service robot domain show that our online relational reinforcement learning method is scalable to large scale problems and enables transfer learning between different problems and simulation environments with dissimilar transition dynamics.