Bellman Error Minimization Via Linear Programming Normalization
On the map: Deep reinforcement learning methods → RL theory and sample complexity also fits: RL for games and combinatorial optimization
Abstract (arXiv)
This paper proposes a new functional approximation approach to reduce Bellman error in high-dimensional dynamic programming and Reinforcement Learning problems. Using a classic dynamic programming problem (network capacity control in revenue management) as the motivational example, the paper illustrates that deep neural networks and linear programming approximation algorithms can be combined to derive approximate solutions to dynamic programming problems. Simulation results show the proposed approximation algorithms achieves competitive performance when compared with benchmark.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 14 references that are in the map. First posted on arXiv 2 Oct 2026.
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1 of 14 references.
References · 1
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
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- 2025Deep neural networks can provably solve Bellman equations for Markov decision processes without the curse of dimensionality
- 2024The Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation
- 2026Lifted Bellman Linear Programming for Offline Reinforcement Learning
- 2024Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics
- 2026Bellman Residual Minimization for Control: Geometry, Stationarity, and Convergence
- 2024Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions
- 2024A priori Estimates for Deep Residual Network in Continuous-time Reinforcement Learning
- 2020Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison