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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 1001–1050 of 1918 papers

TitleStatusHype
Evaluating Load Models and Their Impacts on Power Transfer Limits—0
Evaluating Reinforcement Learning Algorithms for Navigation in Simulated Robotic Quadrupeds: A Comparative Study Inspired by Guide Dog Behaviour—0
Evaluation of Reinforcement Learning Techniques for Trading on a Diverse Portfolio—0
Evaluation of Reinforcement Learning for Autonomous Penetration Testing using A3C, Q-learning and DQN—0
Evolution of cooperation in the public goods game with Q-learning—0
Evolution of Q Values for Deep Q Learning in Stable Baselines—0
Exclusively Penalized Q-learning for Offline Reinforcement Learning—0
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning—0
Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar—0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples—0
Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks—0
Exploration by Maximizing Rényi Entropy for Reward-Free RL Framework—0
Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment—0
Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality—0
Exploration in Knowledge Transfer Utilizing Reinforcement Learning—0
Exploration via Epistemic Value Estimation—0
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning—0
Exploratory Control with Tsallis Entropy for Latent Factor Models—0
Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning—0
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator—0
Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition—0
Fast Adaptive Anti-Jamming Channel Access via Deep Q Learning and Coarse-Grained Spectrum Prediction—0
Fast Block Linear System Solver Using Q-Learning Schduling for Unified Dynamic Power System Simulations—0
Fast constraint satisfaction problem and learning-based algorithm for solving Minesweeper—0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Faster Deep Q-learning using Neural Episodic Control—0
Faster Non-asymptotic Convergence for Double Q-learning—0
Faster Q-Learning Algorithms for Restless Bandits—0
Fastest Convergence for Q-learning—0
Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network—0
Federated Deep Q-Learning and 5G load balancing—0
Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks—0
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices—0
Federated Q-Learning: Linear Regret Speedup with Low Communication Cost—0
Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost—0
Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning—0
FedHQL: Federated Heterogeneous Q-Learning—0
Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning—0
Fictitious play in zero-sum stochastic games—0
Fidelity-based Probabilistic Q-learning for Control of Quantum Systems—0
Final Adaptation Reinforcement Learning for N-Player Games—0
Finding the best design parameters for optical nanostructures using reinforcement learning—0
Finite Horizon Q-learning: Stability, Convergence, Simulations and an application on Smart Grids—0
Finite-Sample Analysis for SARSA with Linear Function Approximation—0
Finite Sample Analysis of Average-Reward TD Learning and Q-Learning—0
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games—0
Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes—0
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation—0
Finite-Time Analysis for Double Q-learning—0
Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learning—0
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