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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 1576–1600 of 1918 papers

TitleStatusHype
A Multi-step and Resilient Predictive Q-learning Algorithm for IoT with Human Operators in the Loop: A Case Study in Water Supply Networks—0
A Multistep Lyapunov Approach for Finite-Time Analysis of Biased Stochastic Approximation—0
An Adiabatic Theorem for Policy Tracking with TD-learning—0
Analysis of Multiscale Reinforcement Q-Learning Algorithms for Mean Field Control Games—0
Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent—0
Analysis of Reinforcement Learning Schemes for Trajectory Optimization of an Aerial Radio Unit—0
Analytically Tractable Bayesian Deep Q-Learning—0
Analytics of Business Time Series Using Machine Learning and Bayesian Inference—0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—0
An Attempt to Model Human Trust with Reinforcement Learning—0
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation—0
An Efficient and Uncertainty-aware Reinforcement Learning Framework for Quality Assurance in Extrusion Additive Manufacturing—0
An efficient data-based off-policy Q-learning algorithm for optimal output feedback control of linear systems—0
An Elementary Proof that Q-learning Converges Almost Surely—0
An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments—0
A Nesterov's Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning—0
A Network Simulation of OTC Markets with Multiple Agents—0
An Evolutionary Framework for Connect-4 as Test-Bed for Comparison of Advanced Minimax, Q-Learning and MCTS—0
A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward—0
A new convergent variant of Q-learning with linear function approximation—0
A new multilayer optical film optimal method based on deep q-learning—0
An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation—0
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning—0
An Independent Study of Reinforcement Learning and Autonomous Driving—0
An Index Policy Based on Sarsa and Q-learning for Heterogeneous Smart Target Tracking—0
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