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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 1301–1350 of 1918 papers

TitleStatusHype
Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networks—0
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient—0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
Trade-off on Sim2Real Learning: Real-world Learning Faster than Simulations—0
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL—0
A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks—0
Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense—0
Same-Day Delivery with Fairness—0
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient LearningCode0
Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment—0
DRIFT: Deep Reinforcement Learning for Functional Software Testing—0
Meta-Gradient Reinforcement Learning with an Objective Discovered Online—0
Qgraph-bounded Q-learning: Stabilizing Model-Free Off-Policy Deep Reinforcement Learning—0
Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent—0
Single-partition adaptive Q-learningCode0
Revisiting Fundamentals of Experience ReplayCode0
The Mean-Squared Error of Double Q-LearningCode0
Hedging using reinforcement learning: Contextual k-Armed Bandit versus Q-learning—0
Group Equivariant Deep Reinforcement LearningCode0
Regularly Updated Deterministic Policy Gradient Algorithm—0
Provably More Efficient Q-Learning in the One-Sided-Feedback/Full-Feedback Settings—0
Using Reinforcement Learning to Herd a Robotic Swarm to a Target Distribution—0
Concept and the implementation of a tool to convert industry 4.0 environments modeled as FSM to an OpenAI Gym wrapper—0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
Lookahead-Bounded Q-LearningCode0
Reinforcement Learning Based Handwritten Digit Recognition with Two-State Q-Learning—0
Offline Contextual Bandits with Overparameterized ModelsCode0
Q-Learning with Differential Entropy of Q-Tables—0
Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity—0
Unified Reinforcement Q-Learning for Mean Field Game and Control Problems—0
Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments—0
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret—0
Near-Optimal Reinforcement Learning with Self-Play—0
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm—0
Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework—0
The Sample Complexity of Teaching-by-Reinforcement on Q-Learning—0
Q-learning with Logarithmic Regret—0
Runtime Adaptation in Wireless Sensor Nodes Using Structured Learning—0
Safety-guaranteed Reinforcement Learning based on Multi-class Support Vector Machine—0
Deep Reinforcement Learning for Neural Control—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Decorrelated Double Q-learning—0
Self-Imitation Learning via Generalized Lower Bound Q-learning—0
Exploration by Maximizing Rényi Entropy for Reward-Free RL Framework—0
Zeroth-Order Supervised Policy Improvement—0
Q-greedyUCB: a New Exploration Policy for Adaptive and Resource-efficient Scheduling—0
Privacy-Cost Management in Smart Meters with Mutual Information-Based Reinforcement Learning—0
Self-Supervised Reinforcement Learning for Recommender Systems—0
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