Learning Process Rewards via Success Visitation Matching for Efficient RL

Researchers propose a novel reinforcement learning approach that converts sparse task rewards into dense process rewards by training a discriminator to identify successful episodes and incentivize policies to match their state-action visitations. The method demonstrates significantly faster training on robotic manipulation tasks without altering the optimal policy.

This research addresses a fundamental challenge in reinforcement learning: the credit assignment problem that arises when rewards are sparse. Traditional RL struggles when feedback only arrives upon task completion, forcing agents to explore inefficiently. The proposed solution uses a discriminator—a common pattern in adversarial machine learning—to serve as a reward shaper, distinguishing between successful and failed trajectories and providing continuous feedback based on behavioral similarity to successful runs.

The approach builds on decades of work in inverse reinforcement learning and reward shaping, but offers a pragmatic advancement. By matching state-action visitations rather than just outcomes, the method provides granular progress signals throughout the learning process. The theoretical guarantee that this transformation preserves optimal policy behavior is particularly important, preventing the common pitfall where reward shaping inadvertently creates perverse incentives.

For robotics and control applications, this has immediate practical value. Finetuning pre-trained policies represents a significant use case where sparse outcome rewards have traditionally bottlenecked performance gains. Faster convergence directly reduces computational costs and real-world robotic training time, both substantial pain points in deploying autonomous systems. The demonstration on both simulated and real hardware suggests the method generalizes beyond toy problems.

The broader implication extends to any domain with sparse outcomes—from autonomous driving to game-playing agents. As RL systems move toward real-world deployment where training iterations are costly, efficient learning mechanisms become critical economic drivers. This work exemplifies how algorithmic innovations can reduce the resource requirements for AI training, making advanced systems more accessible to organizations with limited computational budgets.

• →A discriminator-based approach transforms sparse task rewards into dense process rewards for more efficient RL training.
• →The method preserves optimal policy behavior while providing continuous feedback throughout the learning process.
• →Robotic manipulation tasks showed significantly faster finetuning performance on both simulated and real-world systems.
• →The approach addresses the credit assignment problem by incentivizing policies to match successful episode trajectories.
• →Reduced training time and computational costs make advanced RL systems more accessible for practical applications.