A Unifying Framework for Reinforcement Learning and Planning

TL;DR

This paper introduces a unifying framework called FRAP that identifies common underlying principles in reinforcement learning and planning algorithms, facilitating better understanding and comparison of these approaches in decision-making tasks.

Contribution

The paper presents a novel unifying framework for reinforcement learning and planning, revealing shared dimensions and enabling systematic comparison of algorithms.

Findings

Comparison of planning, model-free, and model-based RL algorithms along key dimensions.

Deeper insights into the algorithmic design space of decision-making methods.

Framework aids in understanding and developing new algorithms.

Abstract

Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimization are reinforcement learning and planning, which both largely have their own research communities. However, if both research fields solve the same problem, then we might be able to disentangle the common factors in their solution approaches. Therefore, this paper presents a unifying algorithmic framework for reinforcement learning and planning (FRAP), which identifies underlying dimensions on which MDP planning and learning algorithms have to decide. At the end of the paper, we compare a variety of well-known planning, model-free and model-based RL algorithms along these dimensions. Altogether, the framework may help provide deeper insight in the algorithmic design space of planning and reinforcement learning.