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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 551–600 of 1918 papers

TitleStatusHype
DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System—0
β-DQN: Improving Deep Q-Learning By Evolving the Behavior—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Bayesian Risk-Averse Q-Learning with Streaming Observations—0
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
Bayesian Q-learning With Imperfect Expert Demonstrations—0
A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning—0
Pretrain Soft Q-Learning with Imperfect Demonstrations—0
BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading—0
Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation—0
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies—0
Double Q-learning: New Analysis and Sharper Finite-time Bound—0
D-Point Trigonometric Path Planning based on Q-Learning in Uncertain Environments—0
DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection—0
Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach—0
Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning—0
Designing Rewards for Fast Learning—0
Bandwidth Reservation for Time-Critical Vehicular Applications: A Multi-Operator Environment—0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation—0
Algorithmic Collusion in Auctions: Evidence from Controlled Laboratory Experiments—0
A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks—0
Double Deep Q-Learning for Optimal Execution—0
Deviations from the Nash equilibrium and emergence of tacit collusion in a two-player optimal execution game with reinforcement learning—0
Design and Comparison of Reward Functions in Reinforcement Learning for Energy Management of Sensor Nodes—0
DGFN: Double Generative Flow Networks—0
Batch Recurrent Q-Learning for Backchannel Generation Towards Engaging Agents—0
DIAR: Diffusion-model-guided Implicit Q-learning with Adaptive Revaluation—0
"Did You Hear That?" Learning to Play Video Games from Audio Cues—0
Differentiable Quantum Architecture Search for Quantum Reinforcement Learning—0
Differentially Private Deep Q-Learning for Pattern Privacy Preservation in MEC Offloading—0
Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation—0
Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task—0
Depth and nonlinearity induce implicit exploration for RL—0
A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets—0
Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning—0
Deploying Reinforcement Learning in Water Transport—0
Digital Twin Assisted Deep Reinforcement Learning for Online Admission Control in Sliced Network—0
Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation—0
Directed Exploration in PAC Model-Free Reinforcement Learning—0
Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach—0
Balancing Two-Player Stochastic Games with Soft Q-Learning—0
A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies—0
Double Deep Q-Learning in Opponent Modeling—0
Density Estimation for Conservative Q-Learning—0
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants—0
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