A Survey of Temporal Credit Assignment in Deep Reinforcement Learning
The Credit Assignment Problem (CAP) refers to the longstanding challenge of Reinforcement Learning (RL) agents to associate actions with their long-term consequences. Solving the CAP is a crucial step towards the successful deployment of RL in the real world since most decision problems provide feedback that is noisy, delayed, and with little or no information about the causes. These conditions make it hard to distinguish serendipitous outcomes from those caused by informed decision-making. However, the mathematical nature of credit and the CAP remains poorly understood and defined. In this survey, we review the state of the art of Temporal Credit Assignment (CA) in deep RL. We propose a unifying formalism for credit that enables equitable comparisons of state-of-the-art algorithms and improves our understanding of the trade-offs between the various methods. We cast the CAP as the problem of learning the influence of an action over an outcome from a finite amount of experience. We discuss the challenges posed by delayed effects, transpositions, and a lack of action influence, and analyse how existing methods aim to address them. Finally, we survey the protocols to evaluate a credit assignment method and suggest ways to diagnose the sources of struggle for different methods. Overall, this survey provides an overview of the field for new-entry practitioners and researchers, it offers a coherent perspective for scholars looking to expedite the starting stages of a new study on the CAP, and it suggests potential directions for future research.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingDeep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)SurveySimilar Papers 제목 키워드 기반
An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning
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 wo…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models
Reinforcement learning (RL) for large language models (LLMs) increasingly relies on sparse, outcome-level rewards -- yet determining which actions within a long trajectory caused the outcome remains difficult. This credi…
Reinforcement LearningEvolutionary Reinforcement Learning: A Survey
Reinforcement learning (RL) is a machine learning approach that trains agents to maximize cumulative rewards through interactions with environments. The integration of RL with deep learning has recently resulted in impre…
Board GamesHyperparameter OptimizationMulti-agent Reinforcement Learningreinforcement-learning+3Selective Credit Assignment
Efficient credit assignment is essential for reinforcement learning algorithms in both prediction and control settings. We describe a unified view on temporal-difference algorithms for selective credit assignment. These …
Predictionreinforcement-learningReinforcement Learning (RL)Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning
Temporal credit assignment in reinforcement learning is challenging due to delayed and stochastic outcomes. Monte Carlo targets can bridge long delays between action and consequence but lead to high-variance targets due …
ChunkingNavigatereinforcement-learningReinforcement Learning