# Towards Practical Credit Assignment for Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2106.04499 · HTML: https://mlsanity.com/paper/2106.04499/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [RUDDER: Return Decomposition for Delayed Rewards](https://mlsanity.com/paper/1806.07857/) (2018)
- [Meta-Gradient Reinforcement Learning](https://mlsanity.com/paper/1805.09801/) (2018)
- [On Learning Intrinsic Rewards for Policy Gradient Methods](https://mlsanity.com/paper/1804.06459/) (2018)
- [Hindsight Credit Assignment](https://mlsanity.com/paper/1912.02503/) (2019)
- [Counterfactual Credit Assignment in Model-Free Reinforcement Learning](https://mlsanity.com/paper/2011.09464/) (2020)
- [Optimizing Agent Behavior over Long Time Scales by Transporting Value](https://mlsanity.com/paper/1810.06721/) (2018)
- [Synthetic Returns for Long-Term Credit Assignment](https://mlsanity.com/paper/2102.12425/) (2021)

## Built on by (most cited first, 5 of 5)

- [BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation](https://mlsanity.com/paper/2403.09227/) (2024)
- [Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis](https://mlsanity.com/paper/2306.16803/) (2023)
- [Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning](https://mlsanity.com/paper/2307.11897/) (2023)
- [Beyond Outcome Reward: Decoupling Search and Answering Improves LLM Agents](https://mlsanity.com/paper/2510.04695/) (2025)
- [Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning](https://mlsanity.com/paper/2405.03878/) (2024)

## Abstract (arXiv)

Credit assignment is a fundamental problem in reinforcement learning, the problem of measuring an action's influence on future rewards. Explicit credit assignment methods have the potential to boost the performance of RL algorithms on many tasks, but thus far remain impractical for general use. Recently, a family of methods called Hindsight Credit Assignment (HCA) was proposed, which explicitly assign credit to actions in hindsight based on the probability of the action having led to an observed outcome. This approach has appealing properties, but remains a largely theoretical idea applicable to a limited set of tabular RL tasks. Moreover, it is unclear how to extend HCA to deep RL environments. In this work, we explore the use of HCA-style credit in a deep RL context. We first describe the limitations of existing HCA algorithms in deep RL that lead to their poor performance or complete lack of training, then propose several theoretically-justified modifications to overcome them. We explore the quantitative and qualitative effects of the resulting algorithm on the Arcade Learning Environment (ALE) benchmark, and observe that it improves performance over Advantage Actor-Critic (A2C) on many games where non-trivial credit assignment is necessary to achieve high scores and where hindsight probabilities can be accurately estimated.