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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 1601–1650 of 1918 papers

TitleStatusHype
Stochastic Lipschitz Q-Learning—0
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning—0
Deep Q-Learning for Nash Equilibria: Nash-DQNCode0
Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net—0
"Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications—0
Patchwork: A Patch-wise Attention Network for Efficient Object Detection and Segmentation in Video Streams—0
Personalized Cancer Chemotherapy Schedule: a numerical comparison of performance and robustness in model-based and model-free scheduling methodologies—0
Learning Automata Based Q-learning for Content Placement in Cooperative Caching—0
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI gamesCode0
Q-Learning for Continuous Actions with Cross-Entropy Guided Policies—0
Towards Characterizing Divergence in Deep Q-Learning—0
Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning—0
Reinforcement Learning with Dynamic Boltzmann Softmax UpdatesCode0
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—0
Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal ControlCode0
Deep Recurrent Q-Learning vs Deep Q-Learning on a simple Partially Observable Markov Decision Process with MinecraftCode0
Successive Over Relaxation Q-Learning—0
Learning Heuristics over Large Graphs via Deep Reinforcement LearningCode0
Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks—0
Unifying Ensemble Methods for Q-learning via Social Choice Theory—0
Diagnosing Bottlenecks in Deep Q-learning AlgorithmsCode0
Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks—0
Distributionally Robust Reinforcement Learning—0
Autonomous Airline Revenue Management: A Deep Reinforcement Learning Approach to Seat Inventory Control and Overbooking—0
Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial PuzzlesCode0
Long and Short Memory Balancing in Visual Co-Tracking using Q-Learning—0
Sample-Optimal Parametric Q-Learning Using Linearly Additive Features—0
Learning Best Response Strategies for Agents in Ad Exchanges—0
Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical SystemsCode0
Finite-Sample Analysis for SARSA with Linear Function Approximation—0
A Theory of Regularized Markov Decision Processes—0
Privacy-preserving Q-Learning with Functional Noise in Continuous State SpacesCode0
Making Deep Q-learning methods robust to time discretizationCode0
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP—0
Provably efficient RL with Rich Observations via Latent State DecodingCode0
Combinational Q-Learning for Dou Di ZhuCode0
Reinforcement Learning of Markov Decision Processes with Peak Constraints—0
Distillation Strategies for Proximal Policy Optimization—0
Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN TargetCode0
A Deep Recurrent Q Network towards Self-adapting Distributed Microservices architectureCode0
Deep Reinforcement Learning for Imbalanced ClassificationCode0
Accelerating Goal-Directed Reinforcement Learning by Model Characterization—0
Optimal Decision-Making in Mixed-Agent Partially Observable Stochastic Environments via Reinforcement Learning—0
Adversarial Learning of a Sampler Based on an Unnormalized DistributionCode0
A Theoretical Analysis of Deep Q-Learning—0
Information-Directed Exploration for Deep Reinforcement LearningCode0
Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing—0
Double Deep Q-Learning for Optimal Execution—0
Learning Sharing Behaviors with Arbitrary Numbers of Agents—0
A new multilayer optical film optimal method based on deep q-learning—0
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