This is the pre-proceedings for the RLC 2026. You may expect minor changes.
Reinforcement Learning Journal, vol. 7, 2026, pp. TBD.
Will be presented at the Reinforcement Learning Conference (RLC), Montréal, Quebec, Canada, August 15–17, 2026.
Keywords: Credit Assignment, Hindsight, Sample-efficiency
Accurately attributing credit to past actions is crucial for sample-efficient reinforcement learning. While temporal-difference learning with $\lambda$-returns is the most commonly used approach, it assigns credit based on the temporal proximity of actions and outcomes---a heuristic that may be overly simple in complex environments. Hindsight-based approaches offer an alternative by using a model that leverages future information to more explicitly credit previous actions that were critical to achieving specific outcomes. Recent work has shown that conditioning hindsight predictions on future rewards, rather than states, can improve credit assignment. However, we show that the associated algorithm, Counterfactual Contribution Analysis (COCOA), suboptimally handles immediate rewards in noisy settings, degenerating into a high-variance estimator even with perfect hindsight. We introduce Reward Hindsight Credit Assignment (R-HCA), which extends hindsight reweighting to immediate rewards. We prove that the variance of R-HCA is no greater than that of COCOA in contextual bandit problems and sparse-reward Markov Decision Processes. Our experiments demonstrate that R-HCA with learned hindsight models outperforms COCOA in domains that require long-term credit assignment with noisy intermediate rewards. We also characterize the conditions under which reward-based hindsight is preferable to standard actor-critic methods.
Aditya A. Ramesh, Jiamin He, Jürgen Schmidhuber, and Martha White. "Improving Reward-Based Hindsight Credit Assignment." Reinforcement Learning Journal, vol. 7, 2026, pp. TBD.
BibTeX:@article{ramesh2026improving,
title={Improving Reward-Based Hindsight Credit Assignment},
author={Aditya A. Ramesh and Jiamin He and Jürgen Schmidhuber and Martha White},
journal={Reinforcement Learning Journal},
volume={7},
pages={},
year={2026}
}