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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 631–640 of 1918 papers

TitleStatusHype
Bootstrapped Hindsight Experience replay with Counterintuitive Prioritization—0
D-Point Trigonometric Path Planning based on Q-Learning in Uncertain Environments—0
DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks—0
DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System—0
DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection—0
DRIFT: Deep Reinforcement Learning for Functional Software Testing—0
DRILL-- Deep Reinforcement Learning for Refinement Operators in ALC—0
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning—0
Breaking the Deadly Triad with a Target Network—0
Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning—0
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