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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

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Showing 1276–1300 of 1918 papers

TitleStatusHype
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based GamesCode0
Is Q-Learning Provably Efficient? An Extended Analysis—0
Hidden Incentives for Auto-Induced Distributional Shift—0
Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing—0
Deep Reinforcement Learning for Option Replication and Hedging—0
AoI Minimization in Status Update Control with Energy Harvesting Sensors—0
PAC Reinforcement Learning Algorithm for General-Sum Markov Games—0
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners—0
A Hybrid PAC Reinforcement Learning Algorithm—0
Learning Nash Equilibria in Zero-Sum Stochastic Games via Entropy-Regularized Policy Approximation—0
Solving the single-track train scheduling problem via Deep Reinforcement Learning—0
Inverse Policy Evaluation for Value-based Sequential Decision-making—0
Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis—0
The reinforcement learning-based multi-agent cooperative approach for the adaptive speed regulation on a metallurgical pickling line—0
Chrome Dino Run using Reinforcement Learning—0
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning—0
Multi-Agent Double Deep Q-Learning for Beamforming in mmWave MIMO Networks—0
Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks—0
Convex Q-Learning, Part 1: Deterministic Optimal Control—0
Evaluating Load Models and Their Impacts on Power Transfer Limits—0
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents—0
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles—0
GenCos' Behaviors Modeling Based on Q Learning Improved by Dichotomy—0
Cooperative Control of Mobile Robots with Stackelberg Learning—0
Momentum Q-learning with Finite-Sample Convergence Guarantee—0
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