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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 51–60 of 1918 papers

TitleStatusHype
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via DiscretisationCode1
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19Code1
A Stochastic Game Framework for Efficient Energy Management in Microgrid NetworksCode1
Addressing Function Approximation Error in Actor-Critic MethodsCode1
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning TechniquesCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised LearningCode1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
Boosting Continuous Control with Consistency PolicyCode1
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