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Methods/DDPG

DDPG

Deep Deterministic Policy Gradient

Reinforcement LearningIntroduced 2000218 papers
Source Paper

Description

DDPG, or Deep Deterministic Policy Gradient, is an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. It combines the actor-critic approach with insights from DQNs: in particular, the insights that 1) the network is trained off-policy with samples from a replay buffer to minimize correlations between samples, and 2) the network is trained with a target Q network to give consistent targets during temporal difference backups. DDPG makes use of the same ideas along with batch normalization.

Papers Using This Method

Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management2025-06-25Reliable Critics: Monotonic Improvement and Convergence Guarantees for Reinforcement Learning2025-06-08A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization2025-06-03LLM-Explorer: A Plug-in Reinforcement Learning Policy Exploration Enhancement Driven by Large Language Models2025-05-21Deep reinforcement learning-based longitudinal control strategy for automated vehicles at signalised intersections2025-05-13Moderate Actor-Critic Methods: Controlling Overestimation Bias via Expectile Loss2025-04-14Intelligent Joint Security and Delay Determinacy Performance Guarantee Strategy in RIS-Assisted IIoT Communication Systems2025-03-11EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning2025-01-25Federated Deep Reinforcement Learning for Energy Efficient Multi-Functional RIS-Assisted Low-Earth Orbit Networks2025-01-19Dynamic Portfolio Optimization via Augmented DDPG with Quantum Price Levels-Based Trading Strategy2025-01-15AutoLoop: Fast Visual SLAM Fine-tuning through Agentic Curriculum Learning2025-01-15An Advantage-based Optimization Method for Reinforcement Learning in Large Action Space2024-12-17Broad Critic Deep Actor Reinforcement Learning for Continuous Control2024-11-24Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning2024-11-20Quantum Policy Gradient in Reproducing Kernel Hilbert Space2024-11-11Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions2024-10-15ETGL-DDPG: A Deep Deterministic Policy Gradient Algorithm for Sparse Reward Continuous Control2024-10-07Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertainty2024-09-27FH-DRL: Exponential-Hyperbolic Frontier Heuristics with DRL for accelerated Exploration in Unknown Environments2024-07-26The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning2024-07-26