# An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning

arXiv: https://arxiv.org/abs/2103.06224 · HTML: https://mlsanity.com/paper/2103.06224/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 75 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Builds on (most cited first, 27 of 27)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [Reinforcement Learning with Deep Energy-Based Policies](https://mlsanity.com/paper/1702.08165/) (2017)
- [Deep Exploration via Bootstrapped DQN](https://mlsanity.com/paper/1602.04621/) (2016)
- [Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review](https://mlsanity.com/paper/1805.00909/) (2018)
- [The Option-Critic Architecture](https://mlsanity.com/paper/1609.05140/) (2016)
- [An Information-Theoretic Analysis of Thompson Sampling](https://mlsanity.com/paper/1403.5341/) (2014)
- [RUDDER: Return Decomposition for Delayed Rewards](https://mlsanity.com/paper/1806.07857/) (2018)
- [Learning to Optimize via Information-Directed Sampling](https://mlsanity.com/paper/1403.5556/) (2014)
- [Learning by Playing - Solving Sparse Reward Tasks from Scratch](https://mlsanity.com/paper/1802.10567/) (2018)
- [Deep Exploration via Randomized Value Functions](https://mlsanity.com/paper/1703.07608/) (2017)
- [Taming the Noise in Reinforcement Learning via Soft Updates](https://mlsanity.com/paper/1512.08562/) (2015)
- [Hindsight Credit Assignment](https://mlsanity.com/paper/1912.02503/) (2019)
- [Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards](https://mlsanity.com/paper/1911.01417/) (2019)
- [Optimizing Agent Behavior over Long Time Scales by Transporting Value](https://mlsanity.com/paper/1810.06721/) (2018)
- [Information asymmetry in KL-regularized RL](https://mlsanity.com/paper/1905.01240/) (2019)
- [Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding](https://mlsanity.com/paper/1809.03702/) (2018)
- [Expected Eligibility Traces](https://mlsanity.com/paper/2007.01839/) (2020)
- [Separating value functions across time-scales](https://mlsanity.com/paper/1902.01883/) (2019)
- [Exploiting Hierarchy for Learning and Transfer in KL-regularized RL](https://mlsanity.com/paper/1903.07438/) (2019)
- [A Unified Bellman Equation for Causal Information and Value in Markov Decision Processes](https://mlsanity.com/paper/1703.01585/) (2017)
- [Capacity of Continuous Channels with Memory via Directed Information Neural Estimator](https://mlsanity.com/paper/2003.04179/) (2020)
- [Self-Attentional Credit Assignment for Transfer in Reinforcement Learning](https://mlsanity.com/paper/1907.08027/) (2019)

## Built on by (most cited first, 20 of 20)

- [A Survey of Temporal Credit Assignment in Deep Reinforcement Learning](https://mlsanity.com/paper/2312.01072/) (2023)
- [Adversarial Intrinsic Motivation for Reinforcement Learning](https://mlsanity.com/paper/2105.13345/) (2021)
- [CAPO: Towards Enhancing LLM Reasoning through Generative Credit Assignment](https://mlsanity.com/paper/2508.02298/) (2025)
- [Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis](https://mlsanity.com/paper/2306.16803/) (2023)
- [Hierarchically Structured Task-Agnostic Continual Learning](https://mlsanity.com/paper/2211.07725/) (2022)
- [Mixture-of-Variational-Experts for Continual Learning](https://mlsanity.com/paper/2110.12667/) (2021)
- [Where Hindsight Credit Can Reside: A Signed-Capacity View of Token Updates in RLVR](https://mlsanity.com/paper/2604.11056/) (2026)
- [The Importance of Credo in Multiagent Learning](https://mlsanity.com/paper/2204.07471/) (2022)
- [Not All Tokens See Equally: Perception-Grounded Policy Optimization for Large Vision-Language Models](https://mlsanity.com/paper/2604.01840/) (2026)
- [On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning](https://mlsanity.com/paper/2210.16877/) (2022)
- [Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction](https://mlsanity.com/paper/2210.16872/) (2022)
- [Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning](https://mlsanity.com/paper/2307.11897/) (2023)
- [Satisficing Exploration for Deep Reinforcement Learning](https://mlsanity.com/paper/2407.12185/) (2024)
- [Bad-Policy Density: A Measure of Reinforcement Learning Hardness](https://mlsanity.com/paper/2110.03424/) (2021)
- [Towards a Better Understanding of Learning with Multiagent Teams](https://mlsanity.com/paper/2306.16205/) (2023)
- [Reflect-R1: Evidence-Driven Reflection for Self-Correction in Long Video Understanding](https://mlsanity.com/paper/2606.27922/) (2026)
- [Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning](https://mlsanity.com/paper/2405.03878/) (2024)
- [Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces](https://mlsanity.com/paper/2306.01896/) (2023)
- [Horizon Reduction as Information Loss in Offline Reinforcement Learning](https://mlsanity.com/paper/2601.00831/) (2025)
- [Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agents](https://mlsanity.com/paper/2609.33875/) (2026)

## Abstract (arXiv)

How do we formalize the challenge of credit assignment in reinforcement learning? Common intuition would draw attention to reward sparsity as a key contributor to difficult credit assignment and traditional heuristics would look to temporal recency for the solution, calling upon the classic eligibility trace. We posit that it is not the sparsity of the reward itself that causes difficulty in credit assignment, but rather the information sparsity. We propose to use information theory to define this notion, which we then use to characterize when credit assignment is an obstacle to efficient learning. With this perspective, we outline several information-theoretic mechanisms for measuring credit under a fixed behavior policy, highlighting the potential of information theory as a key tool towards provably-efficient credit assignment.