# Memory, Space, and Planning: Multiscale Predictive Representations

arXiv: https://arxiv.org/abs/2401.09491 · HTML: https://mlsanity.com/paper/2401.09491/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 131 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 16 of 16)

- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Mastering Atari with Discrete World Models](https://mlsanity.com/paper/2010.02193/) (2020)
- [The Option-Critic Architecture](https://mlsanity.com/paper/1609.05140/) (2016)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Count-Based Exploration with the Successor Representation](https://mlsanity.com/paper/1807.11622/) (2018)
- [A Laplacian Framework for Option Discovery in Reinforcement Learning](https://mlsanity.com/paper/1703.00956/) (2017)
- [Eigenoption Discovery through the Deep Successor Representation](https://mlsanity.com/paper/1710.11089/) (2017)
- [Deep Laplacian-based Options for Temporally-Extended Exploration](https://mlsanity.com/paper/2301.11181/) (2023)
- [Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation](https://mlsanity.com/paper/2105.09637/) (2021)
- [Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods](https://mlsanity.com/paper/2204.11464/) (2022)
- [Social Network Structure Shapes Innovation: Experience-sharing in RL with SAPIENS](https://mlsanity.com/paper/2206.05060/) (2022)
- [Probabilistic Successor Representations with Kalman Temporal Differences](https://mlsanity.com/paper/1910.02532/) (2019)
- [Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation](https://mlsanity.com/paper/1803.09001/) (2018)
- [Neural mechanism to simulate a scale-invariant future timeline](https://mlsanity.com/paper/1503.03322/) (2015)

## Built on by (most cited first, 2 of 2)

- [EvoMem: Improving Multi-Agent Planning with Dual-Evolving Memory](https://mlsanity.com/paper/2511.01912/) (2025)
- [Free Energy Projective Simulation (FEPS): Active inference with interpretability](https://mlsanity.com/paper/2411.14991/) (2024)

## Abstract (arXiv)

Memory is inherently entangled with prediction and planning. Flexible behavior in biological and artificial agents depends on the interplay of learning from the past and predicting the future in ever-changing environments. This chapter reviews computational, behavioral, and neural evidence suggesting these processes rely on learning the relational structure of experiences, known as cognitive maps, and draws two key takeaways. First, that these memory structures are organized as multiscale, compact predictive representations in hippocampal and prefrontal cortex, or PFC, hierarchies. Second, we argue that such predictive memory structures are crucial to the complementary functions of the hippocampus and PFC, both for enabling a recall of detailed and coherent past episodes as well as generalizing experiences at varying scales for efficient prediction and planning. These insights advance our understanding of memory and planning mechanisms in the brain and hold significant implications for advancing artificial intelligence systems.