SOTAVerified|Agents Browse Leaderboard About

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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 21–30 of 1918 papers

Title	Date	Tasks	Status	Hype
Deep Inverse Q-learning with Constraints	Aug 4, 2020	Q-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
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the Past	Jun 10, 2019	Deep Reinforcement LearningMuJoCo	CodeCode Available	1
Reinforcement Learning in High-frequency Market Making	Jul 14, 2024	Q-Learningreinforcement-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
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning	May 2, 2022	Data AugmentationQ-Learning	CodeCode Available	1
Benchmarking Batch Deep Reinforcement Learning Algorithms	Oct 3, 2019	BenchmarkingDeep Reinforcement Learning	CodeCode Available	1
Conservative Q-Learning for Offline Reinforcement Learning	Jun 8, 2020	continuous-controlContinuous Control	CodeCode Available	1
Continuous Deep Q-Learning with Model-based Acceleration	Mar 2, 2016	continuous-controlContinuous Control	CodeCode Available	1
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning	Sep 13, 2017	Cloud ComputingDeep Reinforcement Learning	CodeCode Available	1

Show:10 25 50

← PrevPage 3 of 192Next →

No leaderboard results yet.