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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 1561–1570 of 1918 papers

TitleStatusHype
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection—0
Reinforcement Learning with Low-Complexity Liquid State MachinesCode0
Stabilizing Off-Policy Q-Learning via Bootstrapping Error ReductionCode0
Feature-Based Q-Learning for Two-Player Stochastic Games—0
RSS-Based Q-Learning for Indoor UAV Navigation—0
Provably Efficient Q-Learning with Low Switching Cost—0
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning—0
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology—0
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies—0
Solving NP-Hard Problems on Graphs with Extended AlphaGo ZeroCode0
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