Predictive representations can link model-based reinforcement learning to model-free mechanisms.

ABSTRACT: Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can

SUBMITTER: Russek EM 

PROVIDER: S-EPMC5628940 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature