Toward Efficient Gradient-Based Value Estimation

• URL: http://arxiv.org/abs/2301.13757v3
• Date: Sun, 23 Jul 2023 19:51:25 GMT
• Title: Toward Efficient Gradient-Based Value Estimation
• Authors: Arsalan Sharifnassab, Richard Sutton
• Abstract summary: Gradient-based methods for value estimation in reinforcement learning are typically much slower than Temporal Difference (TD) learning methods. We study the root causes of this slowness and show that Mean Square Bellman Error (MSBE) is an ill-conditioned loss function in the sense that its Hessian has large condition-number. We propose a low complexity batch-free proximal method that approximately follows the Gauss-Newton direction and is robust to parameterization. Our main algorithm, called RANS, is efficient in the sense that it is significantly faster than the residual gradient methods while having almost the same
• Score: 4.365720395124051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gradient-based methods for value estimation in reinforcement learning have favorable stability properties, but they are typically much slower than Temporal Difference (TD) learning methods. We study the root causes of this slowness and show that Mean Square Bellman Error (MSBE) is an ill-conditioned loss function in the sense that its Hessian has large condition-number. To resolve the adverse effect of poor conditioning of MSBE on gradient based methods, we propose a low complexity batch-free proximal method that approximately follows the Gauss-Newton direction and is asymptotically robust to parameterization. Our main algorithm, called RANS, is efficient in the sense that it is significantly faster than the residual gradient methods while having almost the same computational complexity, and is competitive with TD on the classic problems that we tested.

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