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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

Showing 17011750 of 1918 papers

TitleStatusHype
Robbins-Monro conditions for persistent exploration learning strategies0
A Reinforcement Learning Approach to Target Tracking in a Camera Network0
Variational Bayesian Reinforcement Learning with Regret Bounds0
Accelerated Structure-Aware Reinforcement Learning for Delay-Sensitive Energy Harvesting Wireless Sensors0
Remember and Forget for Experience ReplayCode0
Discrete linear-complexity reinforcement learning in continuous action spaces for Q-learning algorithms0
Is Q-learning Provably Efficient?Code1
Video Summarisation by Classification with Deep Reinforcement Learning0
Playing against Nature: causal discovery for decision making under uncertainty0
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems0
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement LearningCode0
Learning to Explore via Meta-Policy Gradient0
Many-Goals Reinforcement Learning0
Reinforcement Learning using Augmented Neural Networks0
Action Learning for 3D Point Cloud Based Organ Segmentation0
Automatic formation of the structure of abstract machines in hierarchical reinforcement learning with state clustering0
Distributional Advantage Actor-Critic0
Fidelity-based Probabilistic Q-learning for Control of Quantum Systems0
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation0
Hyperparameter Optimization for Tracking With Continuous Deep Q-Learning0
Depth and nonlinearity induce implicit exploration for RL0
Hierarchical clustering with deep Q-learning0
Learning Self-Imitating Diverse Policies0
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms0
Learning Sampling Policies for Domain Adaptation0
Algorithmic Trading with Fitted Q Iteration and Heston Model0
GAN Q-learningCode0
Stochastic Approximation for Risk-aware Markov Decision Processes0
Planning and Learning with Stochastic Action Sets0
A Hybrid Q-Learning Sine-Cosine-based Strategy for Addressing the Combinatorial Test Suite Minimization Problem0
Multiagent Soft Q-Learning0
Towards Symbolic Reinforcement Learning with Common SenseCode0
Benchmarking projective simulation in navigation problems0
State Distribution-aware Sampling for Deep Q-learning0
Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision ProblemsCode0
Reinforced Co-Training0
State-Augmentation Transformations for Risk-Sensitive Reinforcement Learning0
CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++Code0
Hierarchical Modular Reinforcement Learning Method and Knowledge Acquisition of State-Action Rule for Multi-target Problem0
Information Maximizing Exploration with a Latent Dynamics Model0
Joint Learning of Interactive Spoken Content Retrieval and Trainable User Simulator0
Deep Reinforcement Learning for Traffic Light Control in Vehicular NetworksCode0
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement LearningCode1
Natural Gradient Deep Q-learning0
Composable Deep Reinforcement Learning for Robotic ManipulationCode0
Learning to Explore with Meta-Policy Gradient0
Multi-Armed Bandits for Correlated Markovian Environments with Smoothed Reward Feedback0
Deep reinforcement learning for time series: playing idealized trading gamesCode0
SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes0
Smoothed Action Value Functions for Learning Gaussian Policies0
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