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Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

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Showing 601–650 of 1918 papers

TitleStatusHype
Depth and nonlinearity induce implicit exploration for RL—0
Distributed 3D-Beam Reforming for Hovering-Tolerant UAVs Communication over Coexistence: A Deep-Q Learning for Intelligent Space-Air-Ground Integrated Networks—0
Distributed Deep Q-Learning—0
Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing—0
Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks—0
A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets—0
Deploying Reinforcement Learning in Water Transport—0
Distributed Multi-Agent Deep Q-Learning for Fast Roaming in IEEE 802.11ax Wi-Fi Systems—0
Distributed Q-Learning with State Tracking for Multi-agent Networked Control—0
Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation—0
BIBI System Description: Building with CNNs and Breaking with Deep Reinforcement Learning—0
Distributional Advantage Actor-Critic—0
Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning—0
Distributionally Robust Reinforcement Learning—0
Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism—0
Distribution-Free Uncertainty Quantification in Mechanical Ventilation Treatment: A Conformal Deep Q-Learning Framework—0
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach—0
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition—0
DO-IQS: Dynamics-Aware Offline Inverse Q-Learning for Optimal Stopping with Unknown Gain Functions—0
Domain Adversarial Reinforcement Learning for Partial Domain Adaptation—0
Double A3C: Deep Reinforcement Learning on OpenAI Gym Games—0
Double Deep Q-Learning-based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications—0
Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach—0
Double Deep Q-Learning for Optimal Execution—0
Balancing Two-Player Stochastic Games with Soft Q-Learning—0
Double Q(σ) and Q(σ, λ): Unifying Reinforcement Learning Control Algorithms—0
A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies—0
Double Q-Learning for Citizen Relocation During Natural Hazards—0
Double Q-learning: New Analysis and Sharper Finite-time Bound—0
Analytically Tractable Bayesian Deep Q-Learning—0
Bootstrapped Hindsight Experience replay with Counterintuitive Prioritization—0
D-Point Trigonometric Path Planning based on Q-Learning in Uncertain Environments—0
DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks—0
DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System—0
DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection—0
DRIFT: Deep Reinforcement Learning for Functional Software Testing—0
DRILL-- Deep Reinforcement Learning for Refinement Operators in ALC—0
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning—0
Breaking the Deadly Triad with a Target Network—0
DRL-Based Dynamic Channel Access and SCLAR Maximization for Networks Under Jamming—0
Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL—0
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning—0
Density Estimation for Conservative Q-Learning—0
Bridging the Gap Between Value and Policy Based Reinforcement Learning—0
Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach—0
Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning With Iterated Q-Learning—0
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants—0
Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques—0
Dynamic Retail Pricing via Q-Learning -- A Reinforcement Learning Framework for Enhanced Revenue Management—0
Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning—0
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