Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis

Alexander Meulemans, Simon Schug, Seijin Kobayashi, Nathaniel D. Daw, Gregory Wayne

Research output: Contribution to journalConference articlepeer-review


To make reinforcement learning more sample efficient, we need better credit assignment methods that measure an action's influence on future rewards.Building upon Hindsight Credit Assignment (HCA) [1], 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, by quantifying a counterfactual query: 'Would the agent still have reached this reward if it had taken another action?'.We show that measuring contributions w.r.t.rewarding states, as is done in HCA, results in spurious estimates of contributions, causing HCA to degrade towards the high-variance REINFORCE estimator in many relevant environments.Instead, we measure contributions w.r.t.rewards or learned representations of the rewarding objects, resulting in gradient estimates with lower variance.We run experiments on a suite of problems specifically designed to evaluate long-term credit assignment capabilities.By using dynamic programming, we measure ground-truth policy gradients and show that the improved performance of our new model-based credit assignment methods is due to lower bias and variance compared to HCA and common baselines.Our results demonstrate how modeling action contributions towards rewarding outcomes can be leveraged for credit assignment, opening a new path towards sample-efficient reinforcement learning.

Original languageEnglish (US)
JournalAdvances in Neural Information Processing Systems
StatePublished - 2023
Event37th Conference on Neural Information Processing Systems, NeurIPS 2023 - New Orleans, United States
Duration: Dec 10 2023Dec 16 2023

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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