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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 626–650 of 1918 papers

TitleStatusHype
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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