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 241–250 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy Approach	Oct 12, 2023	Deep Reinforcement LearningQ-Learning	CodeCode Available	0	5
Learning Simple Algorithms from Examples	Nov 23, 2015	Q-Learning	CodeCode Available	0	5
A DQN-based Approach to Finding Precise Evidences for Fact Verification	Aug 1, 2021	Claim VerificationFact Verification	CodeCode Available	0	5
Learning to Communicate with Deep Multi-Agent Reinforcement Learning	May 21, 2016	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	0	5
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening	Nov 5, 2016	Atari GamesDeep Reinforcement Learning	CodeCode Available	0	5
Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm	Aug 28, 2023	Deep Reinforcement LearningQ-Learning	CodeCode Available	0	5
Active inference: demystified and compared	Sep 24, 2019	Atari GamesOpenAI Gym	CodeCode Available	0	5
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents	Feb 6, 2024	continuous-controlContinuous Control	CodeCode Available	0	5
Introspective Experience Replay: Look Back When Surprised	Jun 7, 2022	Q-Learningreinforcement-learning	CodeCode Available	0	5
Active Collection of Well-Being and Health Data in Mobile Devices	Jul 7, 2023	Q-LearningReinforcement Learning (RL)	CodeCode Available	0	5

Show:10 25 50

← PrevPage 25 of 192Next →

No leaderboard results yet.