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 61–70 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Boosting Continuous Control with Consistency Policy	Oct 10, 2023	continuous-controlContinuous Control	CodeCode Available	1	5
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning	Mar 27, 2018	Deep Reinforcement LearningQ-Learning	CodeCode Available	1	5
Conservative Q-Learning for Offline Reinforcement Learning	Jun 8, 2020	continuous-controlContinuous Control	CodeCode Available	1	5
Continuous control with deep reinforcement learning	Sep 9, 2015	Action Detectioncontinuous-control	CodeCode Available	1	5
Deep Active Inference for Partially Observable MDPs	Sep 8, 2020	Deep Reinforcement LearningQ-Learning	CodeCode Available	1	5
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation	Jun 23, 2021	Continuous ControlQ-Learning	CodeCode Available	1	5
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning	May 2, 2022	Data AugmentationQ-Learning	CodeCode Available	1	5
Addressing Function Approximation Error in Actor-Critic Methods	Feb 26, 2018	Continuous ControlOpenAI Gym	CodeCode Available	1	5
Continuous Deep Q-Learning with Model-based Acceleration	Mar 2, 2016	continuous-controlContinuous Control	CodeCode Available	1	5
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem	Dec 8, 2020	Combinatorial OptimizationQ-Learning	CodeCode Available	1	5

Show:10 25 50

← PrevPage 7 of 192Next →

No leaderboard results yet.