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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 176–200 of 1918 papers

TitleStatusHype
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles—0
A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging—0
Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response—0
A General Framework for Learning Mean-Field Games—0
A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms—0
A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem—0
A storage expansion planning framework using reinforcement learning and simulation-based optimization—0
A short variational proof of equivalence between policy gradients and soft Q learning—0
Adapting Double Q-Learning for Continuous Reinforcement Learning—0
A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks—0
Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning—0
Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning—0
A Flexible Framework for Incorporating Patient Preferences Into Q-Learning—0
ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning—0
Artificial Intelligence and Auction Design—0
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation—0
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation—0
Achieving Stable Training of Reinforcement Learning Agents in Bimodal Environments through Batch Learning—0
A finite time analysis of distributed Q-learning—0
A Finite Sample Complexity Bound for Distributionally Robust Q-learning—0
Active Perception and Representation for Robotic Manipulation—0
An Agile Adaptation Method for Multi-mode Vehicle Communication Networks—0
Artificial Intelligence and Dual Contract—0
A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning—0
Active Measure Reinforcement Learning for Observation Cost Minimization—0
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