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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 1626–1650 of 1918 papers

TitleStatusHype
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
Deep Recurrent Q-Learning vs Deep Q-Learning on a simple Partially Observable Markov Decision Process with MinecraftCode0
Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal ControlCode0
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
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