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 1651–1700 of 1918 papers

TitleStatusHype
Active Deep Q-learning with Demonstration—0
Revisiting the Softmax Bellman Operator: New Benefits and New PerspectiveCode0
Non-delusional Q-learning and value-iteration—0
Urban Driving with Multi-Objective Deep Reinforcement LearningCode0
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy LearningCode0
Reinforcement Learning with A* and a Deep HeuristicCode0
Emergence of Addictive Behaviors in Reinforcement Learning Agents—0
Deep Q learning for fooling neural networksCode0
Managing App Install Ad Campaigns in RTB: A Q-Learning Approach—0
Deep Reinforcement Learning via L-BFGS Optimization—0
Deep Reinforcement Learning for Green Security Games with Real-Time Information—0
Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting—0
Double Q-PID algorithm for mobile robot controlCode0
Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning—0
Structure Learning of Deep Neural Networks with Q-Learning—0
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach—0
Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks—0
Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning—0
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy ImprovementCode0
Optimization of Molecules via Deep Reinforcement LearningCode1
Finding the best design parameters for optical nanostructures using reinforcement learning—0
Assessing the Potential of Classical Q-learning in General Game PlayingCode0
Learning to Sketch with Deep Q Networks and Demonstrated Strokes—0
Learning to Reason—0
Reinforcement Evolutionary Learning Method for self-learning—0
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural NetworksCode0
Deep Quality-Value (DQV) LearningCode0
Reinforcement Learning in R—0
Hybrid Policies Using Inverse Rewards for Reinforcement Learning—0
What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning—0
Accelerated Value Iteration via Anderson Mixing—0
Convergent Reinforcement Learning with Function Approximation: A Bilevel Optimization Perspective—0
A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning—0
The wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions—0
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas—0
Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals—0
Target Transfer Q-Learning and Its Convergence Analysis—0
Model-Free Adaptive Optimal Control of Episodic Fixed-Horizon Manufacturing Processes using Reinforcement LearningCode0
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process—0
Optimal Matrix Momentum Stochastic Approximation and Applications to Q-learning—0
Deterministic Implementations for Reproducibility in Deep Reinforcement LearningCode0
Sampled Policy Gradient for Learning to Play the Game Agar.ioCode0
Towards Better Interpretability in Deep Q-NetworksCode0
Negative Update Intervals in Deep Multi-Agent Reinforcement LearningCode1
Directed Exploration in PAC Model-Free Reinforcement Learning—0
MARL-FWC: Optimal Coordination of Freeway Traffic Control Measures—0
BlockQNN: Efficient Block-wise Neural Network Architecture GenerationCode0
Automatic Derivation Of Formulas Using Reforcement Learning—0
A Framework for Automated Cellular Network Tuning with Reinforcement LearningCode0
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless NetworksCode0
Show:102550
← PrevPage 34 of 39Next →

No leaderboard results yet.