SOTAVerified|Agents Browse Leaderboard About Blog

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 1781–1790 of 1918 papers

Title	Date	Tasks	Status	Hype
A short variational proof of equivalence between policy gradients and soft Q learning	Dec 22, 2017	Q-Learningreinforcement-learning	—Unverified	0
Scale-invariant temporal history (SITH): optimal slicing of the past in an uncertain world	Dec 19, 2017	Q-LearningReinforcement Learning	—Unverified	0
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning	Dec 18, 2017	Deep Reinforcement LearningEvolutionary Algorithms	CodeCode Available	0
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents	Dec 18, 2017	Deep Reinforcement LearningPolicy Gradient Methods	CodeCode Available	0
Towards a Deep Reinforcement Learning Approach for Tower Line Wars	Dec 17, 2017	Deep Reinforcement LearningQ-Learning	—Unverified	0
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds	Dec 13, 2017	BenchmarkingModel-based Reinforcement Learning	CodeCode Available	0
Robust Deep Reinforcement Learning with Adversarial Attacks	Dec 11, 2017	Deep Reinforcement LearningQ-Learning	—Unverified	0
Assumed Density Filtering Q-learning	Dec 9, 2017	Atari GamesBayesian Inference	CodeCode Available	0
Deep Primal-Dual Reinforcement Learning: Accelerating Actor-Critic using Bellman Duality	Dec 7, 2017	Q-Learningreinforcement-learning	—Unverified	0
Zap Q-Learning	Dec 1, 2017	Q-Learning	—Unverified	0

Show:10 25 50

← PrevPage 179 of 192Next →

No leaderboard results yet.