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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 451–500 of 1918 papers

TitleStatusHype
Active inference: demystified and comparedCode0
Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement LearningCode0
Taming the Noise in Reinforcement Learning via Soft UpdatesCode0
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function ApproximationCode0
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic EnvironmentsCode0
Performing Deep Recurrent Double Q-Learning for Atari GamesCode0
A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based ApplicationsCode0
The Effects of Memory Replay in Reinforcement LearningCode0
Agent Performing Autonomous Stock Trading under Good and Bad SituationsCode0
Towards Better Interpretability in Deep Q-NetworksCode0
A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities—0
A Tutorial Introduction to Reinforcement Learning—0
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning—0
A Hybrid Q-Learning Sine-Cosine-based Strategy for Addressing the Combinatorial Test Suite Minimization Problem—0
Adaptive Stochastic Resource Control: A Machine Learning Approach—0
A Theory of Regularized Markov Decision Processes—0
A Theoretical Analysis of Deep Q-Learning—0
A Hybrid PAC Reinforcement Learning Algorithm—0
A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning—0
Asynchronous Stochastic Approximation and Average-Reward Reinforcement Learning—0
A Graph Attention Learning Approach to Antenna Tilt Optimization—0
Adaptive Services Function Chain Orchestration For Digital Health Twin Use Cases: Heuristic-boosted Q-Learning Approach—0
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control—0
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets—0
Unsynchronized Decentralized Q-Learning: Two Timescale Analysis By Persistence—0
Agnostic Q-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity—0
Asymptotics of Reinforcement Learning with Neural Networks—0
Asymptotic regularity of a generalised stochastic Halpern scheme with applications—0
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity—0
Adaptive Q-learning for Interaction-Limited Reinforcement Learning—0
Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net—0
Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamics—0
Deep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement—0
A review of motion planning algorithms for intelligent robotics—0
Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance—0
Deep Primal-Dual Reinforcement Learning: Accelerating Actor-Critic using Bellman Duality—0
Deep Q-Learning for Directed Acyclic Graph Generation—0
A study on a Q-Learning algorithm application to a manufacturing assembly problem—0
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications—0
Deep Q-Learning for Same-Day Delivery with Vehicles and Drones—0
Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement—0
A study of first-passage time minimization via Q-learning in heated gridworlds—0
Deep Q Learning from Dynamic Demonstration with Behavioral Cloning—0
Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market—0
Deep Q-learning of global optimizer of multiply model parameters for viscoelastic imaging—0
Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task—0
Deep Q-Learning with Gradient Target Tracking—0
Deep Q-Learning with Low Switching Cost—0
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment—0
A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning Algorithm—0
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