Deep Deterministic Policy Gradient

Also known as DDPG

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Boundary-aware Reinforcement Learning for Hypercube State Spaces via Deterministic Policy Gradient
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Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies
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University of California, Berkeley · Ecole Polytechnique, CMAP · Imperial College London
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Xiangtan University, Yuhu District Xiangda Road, Xiangtan, 411100, Hunan, China · Peking University, No.5, Summer Palace Road, Haidian District, Beijing, China, Beijing, 100871, Beijing, China
FlowDPG: Deterministic Policy Gradient on Flow Matching Policies for Real-World Manipulation
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Skild AI · Carnegie Mellon University · University of Pennsylvania
Deterministic Policy Gradient for Learning Equilibrium in Time-Inconsistent Control Problems
Jun 10, 2026Xin Guo, Yijie Huang, Xiang YuPolicy Gradient Stochastic Optimal Control
Department of Industrial Engineering and Operations Research, University of California, Berkeley, USA. · Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
Path Planning Using Deep Deterministic Policy Gradient: A Reinforcement Learning Approach
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Hampton University, Hampton, VA 23669, USA · Air Force Research Laboratory, Wright-Patterson AFB, OH 45433, USA
Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning
May 27, 2026Qingwen Pu, Kun Xie, Hong Yang +2Collision Avoidance Pedestrian Behavior Modeling
Transportation Informatics Lab, Department of Civil and Environmental Engineering, Old Dominion University, Norfolk, VA 23529, United States · Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, United States · Department of Transportation and Urban Infrastructure Studies, SMARTER Center, Morgan State University, 1700 E Cold Spring Ln, Baltimore, MD 21251, USA
Identifying Culprits Through Deep Deterministic Policy Gradient Deep Learning Investigation
May 14, 2026Lata B T, Savitha N JDeep Deterministic Policy Gradient
Dept. of CSE, UVCE, Bengaluru, India.
Soft Deterministic Policy Gradient with Gaussian Smoothing
May 7, 2026Hyunjun Na, Donghwan LeeRL Control Policy Gradient
School of Electrical Engineering, KAIST 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea

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Boundary-aware Reinforcement Learning for Hypercube State Spaces via Deterministic Policy Gradient

Lijun Bo, Yijie Huang, Chenhao Lu

RL Control Reinforcement Learning Continuous-Time RL Deep Deterministic Policy Gradient Stochastic Differential Equations

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We develop a continuous-time deterministic policy gradient framework for reinforcement learning with reflected state dynamics, where the state process is governed by a controlled reflected stochastic differential equation on a hypercube. Under suitable regularity assumptions, we establish the connection between the value function and the Neumann Bellman equation, introduce an advantage-rate function that yields a deterministic policy gradient formula, and prove the martingale characterization theorem. Motivated by these theoretical results, we propose a continuous-time deep deterministic policy gradient algorithm for reflected stochastic systems, in which the Neumann boundary condition is imposed via either soft penalization or hard architectural constraint. We further quantify the discrepancy between the ideal continuous-time dynamics and the discretely sampled exploratory dynamics executed in practice, showing that the error decays as the time grid is refined and exploration noise vanishes. Our experiments on reservoir control problems illustrate the effectiveness of the RL framework, highlighting that boundary-aware methods substantially reduce Neumann boundary residuals and enhance learning stability.

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