The Successor Representation and Temporal Context

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Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation (2016, background): “sentation, which represents a value function in terms of the expected future state occupancy. Decomposition of the successor representation yields reasonable sub-goals for spatial navigation problems [5, 12, 44]. Botvinick et al.[3] have written a general overview of hierarchical reinforcement learn”
Deep Successor Reinforcement Learning (2016, background): “Many learning problems involve inferring properties of temporally extended sequences given an objective function.”
Learning Structures: Predictive Representations, Replay, and Generalization (2020, background): “These and other empirical evidence have been taken to suggest the successor representation as a principle for memory organization and temporal context (Figure 1E) [34].”
The successor representation in human reinforcement learning (2016, methodology): “Using these updated representations, we compute Q(sa) at the end of phase 1 and again at the end of phase 2 using equation (5) for the SR learner and equation (6) for the MB learner.”
Design Principles of the Hippocampal Cognitive Map (2014, background): “(3) This recursion can be harnessed to derive a temporal difference learning algorithm for incrementally updating an estimate M̂ of the SR [6, 14].”