Visual Causal-chain Bookmarking: Retrieval-Weighted Credit Assignment for Reinforcement Learning from Delayed Reward
RL agents trained on a single trajectory-level return face a credit-assignment problem: when a reward arrives many steps after the decision that caused it, every action gets the same signal regardless of causal responsibility. This paper studies Visual Causal-chain Bookmarking (VCBM), which concentrates credit on the decision points that determine an episode's return, via two instantiations. The first targets a fully observed, low-dimensional MDP (a delayed-reward watch-repair task), where VCBM statistically discovers which state components the agent causally controls and estimates per-decision-point action values directly, evaluated against ground truth and RUDDER, an LSTM-based baseline...
| Authors | Prathap Selvakumar |
|---|---|
| Primary category | cs.LG |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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