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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
Approximate information state based convergence analysis of recurrent Q-learning—0
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback—0
Approximate Global Convergence of Independent Learning in Multi-Agent Systems—0
Control-Tutored Reinforcement Learning: an application to the Herding Problem—0
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control—0
Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning—0
Applying Reinforcement Learning to Option Pricing and Hedging—0
Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach—0
Active Inference in Hebbian Learning Networks—0
Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty—0
Continuous-time q-learning for mean-field control problems—0
Application of Deep Reinforcement Learning to Payment Fraud—0
Continuous-time q-Learning for Jump-Diffusion Models under Tsallis Entropy—0
Application of Deep Q-Network in Portfolio Management—0
Adversarial Agents For Attacking Inaudible Voice Activated Devices—0
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis—0
Application of Deep Q Learning with Simulation Results for Elevator Optimization—0
APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games—0
Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement—0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts—0
A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens—0
Contextual Conservative Q-Learning for Offline Reinforcement Learning—0
Constructing narrative using a generative model and continuous action policies—0
An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning—0
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