Counterfactual Shapley Credit Assignment
The Credit Assignment Problem (CAP) is fundamental to developing efficient and explainable Reinforcement Learning (RL) agents. Existing frameworks, whether relying on temporal contiguity or hindsight-conditioned reward reweighting, frequently fail to attribute properly between an agent's policy (skill) and environmental stochasticity (luck). A principled approach to CAP must isolate the true causal drivers of observed outcomes from spurious correlations and environmental randomness. We introduce Counterfactual Shapley Credit Assignment, a novel framework grounded in causal theory that attributes credit and blame via the Counterfactual Shapley Value ($φ$-value). By redistributing environme...
| Authors | Mingxuan Li, Kai-Zhan Lee, Elias Bareinboim |
|---|---|
| Primary category | cs.LG |
| Published | 18 July 2026 |
| Last updated at source | 7 August 2026 |
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