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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 1301–1325 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
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