# Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2307.11897 · HTML: https://mlsanity.com/paper/2307.11897/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 60 references that are in the map.
- First posted on arXiv 21 Jul 2023.

## Builds on (most cited first, 30 of 40)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [Doubly Robust Off-policy Value Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1511.03722/) (2015)
- [#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning](https://mlsanity.com/paper/1611.04717/) (2016)
- [Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1604.00923/) (2016)
- [DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections](https://mlsanity.com/paper/1906.04733/) (2019)
- [Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation](https://mlsanity.com/paper/1810.12429/) (2018)
- [Go-Explore: a New Approach for Hard-Exploration Problems](https://mlsanity.com/paper/1901.10995/) (2019)
- [RUDDER: Return Decomposition for Delayed Rewards](https://mlsanity.com/paper/1806.07857/) (2018)
- [Minimax Weight and Q-Function Learning for Off-Policy Evaluation](https://mlsanity.com/paper/1910.12809/) (2019)
- [GenDICE: Generalized Offline Estimation of Stationary Values](https://mlsanity.com/paper/2002.09072/) (2020)
- [Dueling RL: Reinforcement Learning with Trajectory Preferences](https://mlsanity.com/paper/2111.04850/) (2021)
- [An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning](https://mlsanity.com/paper/1503.04269/) (2015)
- [Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search](https://mlsanity.com/paper/1811.06272/) (2018)
- [Hindsight Credit Assignment](https://mlsanity.com/paper/1912.02503/) (2019)
- [GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values](https://mlsanity.com/paper/2001.11113/) (2020)
- [CoinDICE: Off-Policy Confidence Interval Estimation](https://mlsanity.com/paper/2010.11652/) (2020)
- [Counterfactual Credit Assignment in Model-Free Reinforcement Learning](https://mlsanity.com/paper/2011.09464/) (2020)
- [Density Ratio Estimation via Infinitesimal Classification](https://mlsanity.com/paper/2111.11010/) (2021)
- [Optimizing Agent Behavior over Long Time Scales by Transporting Value](https://mlsanity.com/paper/1810.06721/) (2018)
- [Consistent On-Line Off-Policy Evaluation](https://mlsanity.com/paper/1702.07121/) (2017)
- [Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift](https://mlsanity.com/paper/1901.09455/) (2019)
- [Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling](https://mlsanity.com/paper/1910.06508/) (2019)

## Built on by (most cited first, 4 of 4)

- [SSL: Sweet Spot Learning for Differentiated Guidance in Agentic Optimization](https://mlsanity.com/paper/2601.22491/) (2026)
- [MIRA: Memory-Integrated Reinforcement Learning Agent with Limited LLM Guidance](https://mlsanity.com/paper/2602.17930/) (2026)
- [Counterfactual Shapley Credit Assignment](https://mlsanity.com/paper/2607.16999/) (2026)
- [S2T-RLHF: Hierarchical Credit Assignment for Stable Preference-Based RLHF](https://mlsanity.com/paper/2607.18258/) (2026)

## Abstract (arXiv)

Oftentimes, environments for sequential decision-making problems can be quite sparse in the provision of evaluative feedback to guide reinforcement-learning agents. In the extreme case, long trajectories of behavior are merely punctuated with a single terminal feedback signal, leading to a significant temporal delay between the observation of a non-trivial reward and the individual steps of behavior culpable for achieving said reward. Coping with such a credit assignment challenge is one of the hallmark characteristics of reinforcement learning. While prior work has introduced the concept of hindsight policies to develop a theoretically moxtivated method for reweighting on-policy data by impact on achieving the observed trajectory return, we show that these methods experience instabilities which lead to inefficient learning in complex environments. In this work, we adapt existing importance-sampling ratio estimation techniques for off-policy evaluation to drastically improve the stability and efficiency of these so-called hindsight policy methods. Our hindsight distribution correction facilitates stable, efficient learning across a broad range of environments where credit assignment plagues baseline methods.