Goal Agnostic Learning and Planning without Reward Functions

Abstract

In this paper we present an algorithm, the Goal Agnostic Planner (GAP), which combines elements of Reinforcement Learning (RL) and Markov Decision Processes (MDPs) into an elegant, effective system for learning to solve sequential problems. The GAP algorithm does not require the design of either an explicit world model or a reward function to drive policy determination, and is capable of operating on both MDP and RL domain problems. The construction of the GAP lends itself to several analytic guarantees such as policy optimality, exponential goal achievement rates, reciprocal learning rates, measurable robustness to error, and explicit convergence conditions for abstracted states. Empirical results confirm these predictions, demonstrate effectiveness over a wide range of domains, and show that the GAP algorithm performance is an order of magnitude faster than standard reinforcement learning and produces plans of equal quality to MDPs, without requiring design of reward functions.