  A key question in reinforcement learning is how an intelligent agent can
generalize knowledge across different inputs. By generalizing across different
inputs, information learned for one input can be immediately reused for
improving predictions for another input. Reusing information allows an agent to
compute an optimal decision-making strategy using less data. State
representation is a key element of the generalization process, compressing a
high-dimensional input space into a low-dimensional latent state space. This
article analyzes properties of different latent state spaces, leading to new
connections between model-based and model-free reinforcement learning.
Successor features, which predict frequencies of future observations, form a
link between model-based and model-free learning: Learning to predict future
expected reward outcomes, a key characteristic of model-based agents, is
equivalent to learning successor features. Learning successor features is a
form of temporal difference learning and is equivalent to learning to predict a
single policy's utility, which is a characteristic of model-free agents.
Drawing on the connection between model-based reinforcement learning and
successor features, we demonstrate that representations that are predictive of
future reward outcomes generalize across variations in both transitions and
rewards. This result extends previous work on successor features, which is
constrained to fixed transitions and assumes re-learning of the transferred
state representation.

Machine Learning;Artificial Intelligence