Visual Causal-chain Bookmarking: Retrieval-Weighted Credit Assignment for Reinforcement Learning from Delayed Reward

Prathap Selvakumar

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. VCBM converges to 95.07% correct decisions in a mean of 1,532.3 episodes across 20 seeds, versus RUDDER's slower, more variable convergence (mean 3,327.1 episodes; p<0.001); VCBM's discovered causal structure exactly matches ground truth in all seeds. The second targets a partially observed, language-based setting: an LLM agent trained with Leave-One-Out PPO (LOOP) in AppWorld, where causal discovery is intractable and a structural heuristic (state-mutating tool calls) substitutes for it, with…