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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 471–480 of 1918 papers

TitleStatusHype
Boosting Continuous Control with Consistency PolicyCode1
Suppressing Overestimation in Q-Learning through Adversarial Behaviors—0
Dynamic value alignment through preference aggregation of multiple objectives—0
DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather DataCode0
Digital Twin Assisted Deep Reinforcement Learning for Online Admission Control in Sliced Network—0
Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation—0
Applying Reinforcement Learning to Option Pricing and Hedging—0
Optimal Control of District Cooling Energy Plant with Reinforcement Learning and MPC—0
PGDQN: Preference-Guided Deep Q-NetworkCode1
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback—0
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