# Learning Retrospective Knowledge with Reverse Reinforcement Learning

arXiv: https://arxiv.org/abs/2007.06703 · HTML: https://mlsanity.com/paper/2007.06703/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning](https://mlsanity.com/paper/2412.11120/) (2024)
- [Robust Imitation of a Few Demonstrations with a Backwards Model](https://mlsanity.com/paper/2210.09337/) (2022)
- [Autoregressive Modeling with Lookahead Attention](https://mlsanity.com/paper/2305.12272/) (2023)
- [Learning Expected Emphatic Traces for Deep RL](https://mlsanity.com/paper/2107.05405/) (2021)
- [Selective Credit Assignment](https://mlsanity.com/paper/2202.09699/) (2022)
- [From Past to Future: Rethinking Eligibility Traces](https://mlsanity.com/paper/2312.12972/) (2023)
- [A Unified Off-Policy Evaluation Approach for General Value Function](https://mlsanity.com/paper/2107.02711/) (2021)
- [Modeling Human Behavior Part I -- Learning and Belief Approaches](https://mlsanity.com/paper/2205.06485/) (2022)

## Abstract (arXiv)

We present a Reverse Reinforcement Learning (Reverse RL) approach for representing retrospective knowledge. General Value Functions (GVFs) have enjoyed great success in representing predictive knowledge, i.e., answering questions about possible future outcomes such as "how much fuel will be consumed in expectation if we drive from A to B?". GVFs, however, cannot answer questions like "how much fuel do we expect a car to have given it is at B at time t?". To answer this question, we need to know when that car had a full tank and how that car came to B. Since such questions emphasize the influence of possible past events on the present, we refer to their answers as retrospective knowledge. In this paper, we show how to represent retrospective knowledge with Reverse GVFs, which are trained via Reverse RL. We demonstrate empirically the utility of Reverse GVFs in both representation learning and anomaly detection.