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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 61–70 of 1918 papers

Title	Date	Tasks	Status	Hype
Learning Guidance Rewards with Trajectory-space Smoothing	Oct 23, 2020	AttributeDeep Reinforcement Learning	CodeCode Available	1
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning	Mar 27, 2018	Deep Reinforcement LearningQ-Learning	CodeCode Available	1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?	Dec 1, 2020	Feature EngineeringQ-Learning	CodeCode Available	1
Backprop-Free Reinforcement Learning with Active Neural Generative Coding	Jul 10, 2021	Q-Learningreinforcement-learning	CodeCode Available	1
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning	Sep 13, 2017	Cloud ComputingDeep Reinforcement Learning	CodeCode Available	1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning	Jun 7, 2021	Multi-agent Reinforcement LearningOffline RL	CodeCode Available	1
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19	Feb 9, 2021	BenchmarkingQ-Learning	CodeCode Available	1
Addressing Function Approximation Error in Actor-Critic Methods	Feb 26, 2018	Continuous ControlOpenAI Gym	CodeCode Available	1
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the Past	Jun 10, 2019	Deep Reinforcement LearningMuJoCo	CodeCode Available	1
A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning	May 27, 2024	Data AugmentationQ-Learning	CodeCode Available	1

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