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 1491–1500 of 1918 papers

TitleStatusHype
QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error—0
Off-policy Multi-step Q-learning—0
Modeling Fake News in Social Networks with Deep Multi-Agent Reinforcement Learning—0
Long-term planning, short-term adjustments—0
Striving for Simplicity in Off-Policy Deep Reinforcement Learning—0
CAN ALTQ LEARN FASTER: EXPERIMENTS AND THEORY—0
Policy Tree Network—0
Active inference: demystified and comparedCode0
On the Convergence of Approximate and Regularized Policy Iteration Schemes—0
Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach—0
Show:102550
← PrevPage 150 of 192Next →

No leaderboard results yet.