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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 1201–1250 of 1918 papers

TitleStatusHype
Stabilizing Transformer-Based Action Sequence Generation For Q-Learning—0
Deep Surrogate Q-Learning for Autonomous Driving—0
Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets—0
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality—0
Logistic Q-Learning—0
Language Inference with Multi-head Automata through Reinforcement Learning—0
Learning Dexterous Manipulation from Suboptimal Experts—0
Multi-Agent Collaboration via Reward Attribution DecompositionCode1
A Nesterov's Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning—0
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control—0
Parameterized Reinforcement Learning for Optical System Optimization—0
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological ModelsCode1
Q-learning with Language Model for Edit-based Unsupervised SummarizationCode1
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic EnvironmentsCode0
Fictitious play in zero-sum stochastic games—0
Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control—0
Machine Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Wireless Networks—0
Cross Learning in Deep Q-Networks—0
Finite-Time Analysis for Double Q-learning—0
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning—0
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space—0
Near-Optimal Regret Bounds for Model-Free RL in Non-Stationary Episodic MDPs—0
A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward—0
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
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
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
Deep Active Inference for Partially Observable MDPsCode1
A Hybrid PAC Reinforcement Learning Algorithm—0
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners—0
PAC Reinforcement Learning Algorithm for General-Sum Markov Games—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
Table2Charts: Recommending Charts by Learning Shared Table RepresentationsCode1
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
Robust Deep Reinforcement Learning through Adversarial LossCode1
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles—0
Deep Inverse Q-learning with ConstraintsCode1
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