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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 801–850 of 1918 papers

TitleStatusHype
Generative Multi-Agent Q-Learning for Policy Optimization: Decentralized Wireless Networks—0
Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems—0
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control—0
GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits—0
G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning—0
Control-Tutored Reinforcement Learning: an application to the Herding Problem—0
Deep Spectral Q-learning with Application to Mobile Health—0
Approximate Global Convergence of Independent Learning in Multi-Agent Systems—0
Gradient Q(σ, λ): A Unified Algorithm with Function Approximation for Reinforcement Learning—0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning—0
Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery—0
Graph Exploration for Effective Multi-agent Q-Learning—0
Graph Neural Network based Agent in Google Research Football—0
Graph Q-Learning for Combinatorial Optimization—0
Greedy-Step Off-Policy Reinforcement Learning—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
Convergent and Efficient Deep Q Learning Algorithm—0
Approximate Nash Equilibrium Learning for n-Player Markov Games in Dynamic Pricing—0
Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution—0
Guiding Reinforcement Learning Exploration Using Natural Language—0
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension—0
Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time—0
A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning—0
Convert Language Model into a Value-based Strategic Planner—0
Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration—0
Deep Robot Sketching: An application of Deep Q-Learning Networks for human-like sketching—0
HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree Search—0
Hedging of Financial Derivative Contracts via Monte Carlo Tree Search—0
Hedging using reinforcement learning: Contextual k-Armed Bandit versus Q-learning—0
Cooperation and Reputation Dynamics with Reinforcement Learning—0
Hidden Incentives for Auto-Induced Distributional Shift—0
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process—0
Hierarchical clustering with deep Q-learning—0
Cooperative Control of Mobile Robots with Stackelberg Learning—0
Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity—0
Hierarchical Modular Reinforcement Learning Method and Knowledge Acquisition of State-Action Rule for Multi-target Problem—0
Cooperative Optimal Output Tracking for Discrete-Time Multiagent Systems: Stabilizing Policy Iteration Frameworks and Analysis—0
High dimensional precision medicine from patient-derived xenografts—0
High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning—0
Highway Reinforcement Learning—0
Hippocampal representations emerge when training recurrent neural networks on a memory dependent maze navigation task—0
How to discretize continuous state-action spaces in Q-learning: A symbolic control approach—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm—0
Hybrid LLM-DDQN based Joint Optimization of V2I Communication and Autonomous Driving—0
Hybrid Policies Using Inverse Rewards for Reinforcement Learning—0
Hybrid Q-Learning Applied to Ubiquitous recommender system—0
A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts—0
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