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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 276–300 of 1918 papers

TitleStatusHype
Stabilizing Extreme Q-learning by Maclaurin ExpansionCode0
Strategically Conservative Q-LearningCode1
Bootstrapping Expectiles in Reinforcement Learning—0
Age of Trust (AoT): A Continuous Verification Framework for Wireless Networks—0
Algorithmic Collusion in Dynamic Pricing with Deep Reinforcement Learning—0
Towards Universal and Black-Box Query-Response Only Attack on LLMs with QROACode1
Tabular and Deep Learning for the Whittle Index—0
How to discretize continuous state-action spaces in Q-learning: A symbolic control approach—0
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function ApproximationCode0
Approximate Global Convergence of Independent Learning in Multi-Agent Systems—0
Q-learning as a monotone scheme—0
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost—0
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted RegressionCode0
Safe Multi-Agent Reinforcement Learning with Bilevel Optimization in Autonomous DrivingCode2
AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained OptimizationCode0
Highway Reinforcement Learning—0
Mutation-Bias Learning in Games—0
A Recipe for Unbounded Data Augmentation in Visual Reinforcement LearningCode1
Analysis of Multiscale Reinforcement Q-Learning Algorithms for Mean Field Control Games—0
Reinforcement Learning for Jump-Diffusions, with Financial Applications—0
An Evolutionary Framework for Connect-4 as Test-Bed for Comparison of Advanced Minimax, Q-Learning and MCTS—0
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning—0
Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine—0
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
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