  Recent work has uncovered close links between between classical reinforcement
learning algorithms, Bayesian filtering, and Active Inference which lets us
understand value functions in terms of Bayesian posteriors. An alternative, but
less explored, model-free RL algorithm is the successor representation, which
expresses the value function in terms of a successor matrix of expected future
state occupancies. In this paper, we derive the probabilistic interpretation of
the successor representation in terms of Bayesian filtering and thus design a
novel active inference agent architecture utilizing successor representations
instead of model-based planning. We demonstrate that active inference successor
representations have significant advantages over current active inference
agents in terms of planning horizon and computational cost. Moreover, we
demonstrate how the successor representation agent can generalize to changing
reward functions such as variants of the expected free energy.
