State Flow Credit Assignment: Improving Credit Assignment with Generative Modeling

Credit assignment is a fundamental challenge in reinforcement learning and is critical for both learning stability and policy optimality. Prior work has explored counterfactual and hindsight approaches, which typically adopt a retrospective perspective, modeling influence factors from future trajectories. As a result, the credit assigned to individual actions can be entangled with the effects of subsequent actions, making it difficult to isolate their true contributions. To address this challenge, we propose State Flow Credit Assignment (SFCA), a novel framework that integrates generative modeling into the credit assignment process. SFCA explicitly models the influence of individual actions on future states. It represents an action’s effect as the induced difference between future states and quantifies this difference to assign credit to the action. Experimental results demonstrate that SFCA leads to consistent improvements over baselines across diverse environments and achieves particularly significant gains in tasks where baseline performance plateaus.