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 1411–1420 of 1918 papers

TitleStatusHype
VA-learning as a more efficient alternative to Q-learning—0
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings—0
Value function interference and greedy action selection in value-based multi-objective reinforcement learning—0
Value-of-Information based Arbitration between Model-based and Model-free Control—0
Value Penalized Q-Learning for Recommender Systems—0
Value Refinement Network (VRN)—0
Vanishing Bias Heuristic-guided Reinforcement Learning Algorithm—0
Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity—0
Variance-reduced Q-learning is minimax optimal—0
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient—0
Show:102550
← PrevPage 142 of 192Next →

No leaderboard results yet.