SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1021–1030 of 1918 papers

TitleStatusHype
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs—0
Value Penalized Q-Learning for Recommender Systems—0
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication—0
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Fast Block Linear System Solver Using Q-Learning Schduling for Unified Dynamic Power System Simulations—0
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games—0
Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning—0
Urban traffic dynamic rerouting framework: A DRL-based model with fog-cloud architecture—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning—0
Show:102550
← PrevPage 103 of 192Next →

No leaderboard results yet.