Related papers: An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning

An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning

URL: http://arxiv.org/abs/2103.06224v1
Date: Wed, 10 Mar 2021 17:50:15 GMT
Title: An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning
Authors: Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon
Abstract summary: We argue that it is not the sparsity of the reward itself that causes difficulty in credit assignment, but rather the emph information sparsity 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.
Score: 14.367867691822026
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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 \emph{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.

Related papers

Best Practices for Responsible Machine Learning in Credit Scoring [0.03984353141309896]
This tutorial paper performed a non-systematic literature review to guide best practices for developing responsible machine learning models in credit scoring. We discuss definitions, metrics, and techniques for mitigating biases and ensuring equitable outcomes across different groups. By adopting these best practices, financial institutions can harness the power of machine learning while upholding ethical and responsible lending practices.
arXiv Detail & Related papers (2024-09-30T17:39:38Z)
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning [47.17998784925718]
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.
arXiv Detail & Related papers (2023-12-02T08:49:51Z)
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)
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)
Selective Credit Assignment [57.41789233550586]
We describe a unified view on temporal-difference algorithms for selective credit assignment. We present insights into applying weightings to value-based learning and planning algorithms.
arXiv Detail & Related papers (2022-02-20T00:07:57Z)
Direct Advantage Estimation [63.52264764099532]
We show that the expected return may depend on the policy in an undesirable way which could slow down learning. We propose the Direct Advantage Estimation (DAE), a novel method that can model the advantage function and estimate it directly from data. If desired, value functions can also be seamlessly integrated into DAE and be updated in a similar way to Temporal Difference Learning.
arXiv Detail & Related papers (2021-09-13T16:09:31Z)
Explanations of Machine Learning predictions: a mandatory step for its application to Operational Processes [61.20223338508952]
Credit Risk Modelling plays a paramount role. Recent machine and deep learning techniques have been applied to the task. We suggest to use LIME technique to tackle the explainability problem in this field.
arXiv Detail & Related papers (2020-12-30T10:27:59Z)
Explainable AI for Interpretable Credit Scoring [0.8379286663107844]
Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application. Regulations have added the need for model interpretability to ensure that algorithmic decisions are understandable coherent. We present a credit scoring model that is both accurate and interpretable.
arXiv Detail & Related papers (2020-12-03T18:44:03Z)
Counterfactual Credit Assignment in Model-Free Reinforcement Learning [47.79277857377155]
Credit assignment in reinforcement learning is the problem of measuring an action's influence on future rewards. We adapt the notion of counterfactuals from causality theory to a model-free RL setup. We formulate a family of policy algorithms that use future-conditional value functions as baselines or critics, and show that they are provably low variance.
arXiv Detail & Related papers (2020-11-18T18:41:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.