A Survey of Temporal Credit Assignment in Deep Reinforcement Learning
- URL: http://arxiv.org/abs/2312.01072v2
- Date: Thu, 4 Jul 2024 09:32:18 GMT
- Title: A Survey of Temporal Credit Assignment in Deep Reinforcement Learning
- Authors: Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, Olivier Pietquin, Laura Toni,
- Abstract summary: The Credit Assignment Problem (CAP) refers to the longstanding challenge of Reinforcement Learning (RL) agents to associate actions with their long-term consequences.
We propose a unifying formalism for credit that enables equitable comparisons of state-of-the-art algorithms.
We discuss the challenges posed by delayed effects, transpositions, and a lack of action influence, and analyse how existing methods aim to address them.
- Score: 47.17998784925718
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review [50.67937325077047]
This paper is devoted to a comprehensive review of realizing the sample efficiency and generalization of RL algorithms through transfer and inverse reinforcement learning (T-IRL)
Our findings denote that a majority of recent research works have dealt with the aforementioned challenges by utilizing human-in-the-loop and sim-to-real strategies.
Under the IRL structure, training schemes that require a low number of experience transitions and extension of such frameworks to multi-agent and multi-intention problems have been the priority of researchers in recent years.
arXiv Detail & Related papers (2024-11-15T15:18:57Z) - Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment [56.87031484108484]
Large Language Models (LLMs) are increasingly recognized for their practical applications.
Retrieval-Augmented Generation (RAG) tackles this challenge and has shown a significant impact on LLMs.
By minimizing retrieval requests that yield neutral or harmful results, we can effectively reduce both time and computational costs.
arXiv Detail & Related papers (2024-11-09T15:12:28Z) - Towards CausalGPT: A Multi-Agent Approach for Faithful Knowledge Reasoning via Promoting Causal Consistency in LLMs [60.244412212130264]
Causal-Consistency Chain-of-Thought harnesses multi-agent collaboration to bolster the faithfulness and causality of foundation models.
Our framework demonstrates significant superiority over state-of-the-art methods through extensive and comprehensive evaluations.
arXiv Detail & Related papers (2023-08-23T04:59:21Z) - Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning [11.084321518414226]
We adapt existing importance-sampling ratio estimation techniques for off-policy evaluation to drastically improve the stability and efficiency of 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.
arXiv Detail & Related papers (2023-07-21T20:54:52Z) - Would I have gotten that reward? Long-term credit assignment by
counterfactual contribution analysis [50.926791529605396]
We introduce Counterfactual Contribution Analysis (COCOA), a new family of model-based credit assignment algorithms.
Our algorithms achieve precise credit assignment by measuring the contribution of actions upon obtaining subsequent rewards.
arXiv Detail & Related papers (2023-06-29T09:27:27Z) - Reinforcement Learning with Knowledge Representation and Reasoning: A
Brief Survey [24.81327556378729]
Reinforcement Learning has achieved tremendous development in recent years.
Still faces significant obstacles in addressing complex real-life problems.
Recently, there has been a rapidly growing interest in the use of Knowledge Representation and Reasoning.
arXiv Detail & Related papers (2023-04-24T13:35:11Z) - A Survey on Causal Reinforcement Learning [41.645270300009436]
We offer a review of Causal Reinforcement Learning (CRL) works, offer a review of CRL methods, and investigate the potential functionality from causality toward RL.
In particular, we divide existing CRL approaches into two categories according to whether their causality-based information is given in advance or not.
We analyze each category in terms of the formalization of different models, ranging from the Markov Decision Process (MDP), Partially Observed Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment Regime (DTR)
arXiv Detail & Related papers (2023-02-10T12:25:08Z) - Towards Causal Credit Assignment [0.0]
Hindsight Credit Assignment is a promising, but still unexplored candidate, which aims to solve the problems of both long-term and counterfactual credit assignment.
In this thesis, we empirically investigate Hindsight Credit Assignment to identify its main benefits, and key points to improve.
We show that our modification greatly decreases the workload of Hindsight Credit Assignment, making it more efficient and enabling it to outperform the baseline credit assignment method on various tasks.
arXiv Detail & Related papers (2022-12-22T12:06:37Z) - Inverse Online Learning: Understanding Non-Stationary and Reactionary
Policies [79.60322329952453]
We show how to develop interpretable representations of how agents make decisions.
By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem.
We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them.
Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
arXiv Detail & Related papers (2022-03-14T17:40:42Z) - Towards Practical Credit Assignment for Deep Reinforcement Learning [0.6749750044497732]
Credit assignment is a fundamental problem in reinforcement learning.
Recently, a family of methods called Hindsight Credit Assignment (HCA) was proposed, which explicitly assign credit to actions in hindsight.
We present a new algorithm, Credit-Constrained Advantage Actor-Critic (C2A2C), which ignores policy updates for actions which don't affect future outcomes based on credit in hindsight.
arXiv Detail & Related papers (2021-06-08T16:35:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.