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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 726–750 of 1918 papers

TitleStatusHype
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
A Deep Reinforcement Learning Trader without Offline Training—0
Action-modulated midbrain dopamine activity arises from distributed control policies—0
Accelerated Target Updates for Q-learning—0
Equivalence Between Policy Gradients and Soft Q-Learning—0
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks—0
Fast Adaptive Anti-Jamming Channel Access via Deep Q Learning and Coarse-Grained Spectrum Prediction—0
Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning—0
Chrome Dino Run using Reinforcement Learning—0
Entropy-Augmented Entropy-Regularized Reinforcement Learning and a Continuous Path from Policy Gradient to Q-Learning—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
Entropic Risk Optimization in Discounted MDPs: Sample Complexity Bounds with a Generative Model—0
Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network—0
Chemoreception and chemotaxis of a three-sphere swimmer—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
Ensemble Bootstrapping for Q-Learning—0
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