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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 376–400 of 1918 papers

TitleStatusHype
Concept and the implementation of a tool to convert industry 4.0 environments modeled as FSM to an OpenAI Gym wrapper—0
Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting—0
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach—0
An Efficient and Uncertainty-aware Reinforcement Learning Framework for Quality Assurance in Extrusion Additive Manufacturing—0
Consecutive Task-oriented Dialog Policy Learning—0
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models—0
Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning With Iterated Q-Learning—0
CoNSoLe: Convex Neural Symbolic Learning—0
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation—0
Constrained Model-Free Reinforcement Learning for Process Optimization—0
Constraints Penalized Q-learning for Safe Offline Reinforcement Learning—0
Constructing narrative using a generative model and continuous action policies—0
Contextual Conservative Q-Learning for Offline Reinforcement Learning—0
A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens—0
Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts—0
Bridging the Gap Between Value and Policy Based Reinforcement Learning—0
APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games—0
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis—0
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation—0
Application of Deep Q-Network in Portfolio Management—0
Continuous-time q-Learning for Jump-Diffusion Models under Tsallis Entropy—0
Continuous-time q-learning for mean-field control problems—0
Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty—0
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning—0
Breaking the Deadly Triad with a Target Network—0
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