12 research outputs found
A Framework for Reinforcement Learning and Planning
Sequential decision making, commonly formalized as Markov Decision Process
optimization, is a key challenge in artificial intelligence. Two successful
approaches to MDP optimization are planning and reinforcement learning. Both
research fields largely have their own research communities. However, if both
research fields solve the same problem, then we should be able to disentangle
the common factors in their solution approaches. Therefore, this paper presents
a unifying framework for reinforcement learning and planning (FRAP), which
identifies the underlying dimensions on which any planning or learning
algorithm has to decide. At the end of the paper, we compare - in a single
table - a variety of well-known planning, model-free and model-based RL
algorithms along the dimensions of our framework, illustrating the validity of
the framework. Altogether, FRAP provides deeper insight into the algorithmic
space of planning and reinforcement learning, and also suggests new approaches
to integration of both fields