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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 1701–1750 of 1918 papers

TitleStatusHype
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas—0
Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals—0
Target Transfer Q-Learning and Its Convergence Analysis—0
Model-Free Adaptive Optimal Control of Episodic Fixed-Horizon Manufacturing Processes using Reinforcement LearningCode0
Optimal Matrix Momentum Stochastic Approximation and Applications to Q-learning—0
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process—0
Deterministic Implementations for Reproducibility in Deep Reinforcement LearningCode0
Sampled Policy Gradient for Learning to Play the Game Agar.ioCode0
Towards Better Interpretability in Deep Q-NetworksCode0
Directed Exploration in PAC Model-Free Reinforcement Learning—0
MARL-FWC: Optimal Coordination of Freeway Traffic Control Measures—0
BlockQNN: Efficient Block-wise Neural Network Architecture GenerationCode0
Automatic Derivation Of Formulas Using Reforcement Learning—0
A Framework for Automated Cellular Network Tuning with Reinforcement LearningCode0
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless NetworksCode0
Robbins-Monro conditions for persistent exploration learning strategies—0
A Reinforcement Learning Approach to Target Tracking in a Camera Network—0
Variational Bayesian Reinforcement Learning with Regret Bounds—0
Accelerated Structure-Aware Reinforcement Learning for Delay-Sensitive Energy Harvesting Wireless Sensors—0
Discrete linear-complexity reinforcement learning in continuous action spaces for Q-learning algorithms—0
Remember and Forget for Experience ReplayCode0
Video Summarisation by Classification with Deep Reinforcement Learning—0
Playing against Nature: causal discovery for decision making under uncertainty—0
Learning to Explore via Meta-Policy Gradient—0
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement LearningCode0
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems—0
Many-Goals Reinforcement Learning—0
Reinforcement Learning using Augmented Neural Networks—0
Action Learning for 3D Point Cloud Based Organ Segmentation—0
Automatic formation of the structure of abstract machines in hierarchical reinforcement learning with state clustering—0
Distributional Advantage Actor-Critic—0
Fidelity-based Probabilistic Q-learning for Control of Quantum Systems—0
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation—0
Hyperparameter Optimization for Tracking With Continuous Deep Q-Learning—0
Depth and nonlinearity induce implicit exploration for RL—0
Hierarchical clustering with deep Q-learning—0
Learning Self-Imitating Diverse Policies—0
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms—0
Learning Sampling Policies for Domain Adaptation—0
Algorithmic Trading with Fitted Q Iteration and Heston Model—0
GAN Q-learningCode0
Stochastic Approximation for Risk-aware Markov Decision Processes—0
Planning and Learning with Stochastic Action Sets—0
A Hybrid Q-Learning Sine-Cosine-based Strategy for Addressing the Combinatorial Test Suite Minimization Problem—0
Multiagent Soft Q-Learning—0
Benchmarking projective simulation in navigation problems—0
Towards Symbolic Reinforcement Learning with Common SenseCode0
State Distribution-aware Sampling for Deep Q-learning—0
Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision ProblemsCode0
Reinforced Co-Training—0
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