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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 826–850 of 1918 papers

TitleStatusHype
A new multilayer optical film optimal method based on deep q-learning—0
A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control—0
Action Learning for 3D Point Cloud Based Organ Segmentation—0
HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree Search—0
Hedging of Financial Derivative Contracts via Monte Carlo Tree Search—0
Hedging using reinforcement learning: Contextual k-Armed Bandit versus Q-learning—0
Enhanced Deep Q-Learning for 2D Self-Driving Cars: Implementation and Evaluation on a Custom Track Environment—0
Energy Sharing for Multiple Sensor Nodes with Finite Buffers—0
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process—0
Hierarchical clustering with deep Q-learning—0
Cell Switching in HAPS-Aided Networking: How the Obscurity of Traffic Loads Affects the Decision—0
Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity—0
Hierarchical Modular Reinforcement Learning Method and Knowledge Acquisition of State-Action Rule for Multi-target Problem—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
High dimensional precision medicine from patient-derived xenografts—0
High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning—0
Highway Reinforcement Learning—0
Hippocampal representations emerge when training recurrent neural networks on a memory dependent maze navigation task—0
How to discretize continuous state-action spaces in Q-learning: A symbolic control approach—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm—0
Energy-Efficient Power Allocation and Q-Learning-Based Relay Selection for Relay-Aided D2D Communication—0
Hybrid Policies Using Inverse Rewards for Reinforcement Learning—0
Hybrid Q-Learning Applied to Ubiquitous recommender system—0
A new convergent variant of Q-learning with linear function approximation—0
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