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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 901–925 of 1918 papers

TitleStatusHype
Deep Reinforcement Learning for Multi-class Imbalanced TrainingCode0
Optimizing Returns Using the Hurst Exponent and Q Learning on Momentum and Mean Reversion Strategies—0
Reinforced Pedestrian Attribute Recognition with Group Optimization Reward—0
Parallel bandit architecture based on laser chaos for reinforcement learning—0
Efficient Off-Policy Reinforcement Learning via Brain-Inspired Computing—0
Representation Learning for Context-Dependent Decision-Making—0
Final Iteration Convergence Bound of Q-Learning: Switching System Approach—0
Characterizing the Action-Generalization Gap in Deep Q-Learning—0
Neuromimetic Linear Systems -- Resilience and Learning—0
Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic MethodsCode0
Vehicle management in a modular production context using Deep Q-Learning—0
Chemoreception and chemotaxis of a three-sphere swimmer—0
Q-Learning Scheduler for Multi Task Learning Through the use of Histogram of Task Uncertainty—0
Learning Value Functions from Undirected State-only Experience—0
Graph Neural Network based Agent in Google Research Football—0
Provably Efficient Kernelized Q-Learning—0
Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics—0
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning—0
Optimizing the Long-Term Behaviour of Deep Reinforcement Learning for Pushing and Grasping—0
Q-learning with online random forests—0
Deep Q-learning of global optimizer of multiply model parameters for viscoelastic imaging—0
Neural Q-learning for solving PDEs—0
Functional Stability of Discounted Markov Decision Processes Using Economic MPC Dissipativity Theory—0
Investigating the Properties of Neural Network Representations in Reinforcement Learning—0
Topological Experience ReplayCode0
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