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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 701–750 of 1918 papers

TitleStatusHype
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks—0
Equivalence Between Policy Gradients and Soft Q-Learning—0
Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning—0
Balancing a CartPole System with Reinforcement Learning -- A Tutorial—0
C-Learning: Learning to Achieve Goals via Recursive Classification—0
Evaluating Load Models and Their Impacts on Power Transfer Limits—0
ShiQ: Bringing back Bellman to LLMs—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
Collaborative Deep Reinforcement Learning for Joint Object Search—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
Balanced Q-learning: Combining the Influence of Optimistic and Pessimistic Targets—0
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear—0
Deep Surrogate Q-Learning for Autonomous Driving—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
Combining policy gradient and Q-learning—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
Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise—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
Comparing NARS and Reinforcement Learning: An Analysis of ONA and Q-Learning Algorithms—0
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation—0
Finite-Time Error Analysis of Online Model-Based Q-Learning with a Relaxed Sampling Model—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
Fire Threat Detection From Videos with Q-Rough Sets—0
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