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 51–60 of 1918 papers

Title	Date	Tasks	Status	Hype
Robust Q-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty	Sep 30, 2022	Q-Learning	CodeCode Available	1
Revisiting Discrete Soft Actor-Critic	Sep 21, 2022	Atari GamesQ-Learning	CodeCode Available	1
MAN: Multi-Action Networks Learning	Sep 19, 2022	Atari GamesDeep Reinforcement Learning	CodeCode Available	1
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games	Jul 18, 2022	Atari GamesDeep Reinforcement Learning	CodeCode Available	1
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning	Jul 12, 2022	Lifelong learningPolicy Gradient Methods	CodeCode Available	1
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman Problems	Jul 8, 2022	Combinatorial OptimizationQ-Learning	CodeCode Available	1
On the Learning and Learnability of Quasimetrics	Jun 30, 2022	Q-LearningReinforcement Learning (RL)	CodeCode Available	1
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer	Jun 20, 2022	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration	Jun 20, 2022	Atari GamesDeep Reinforcement Learning	CodeCode Available	1
A Search-Based Testing Approach for Deep Reinforcement Learning Agents	Jun 15, 2022	Autonomous DrivingDecision Making	CodeCode Available	1

Show:10 25 50

← PrevPage 6 of 192Next →

No leaderboard results yet.