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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 1121–1130 of 1918 papers

TitleStatusHype
Reinforcement Learning-based Joint Path and Energy Optimization of Cellular-Connected Unmanned Aerial Vehicles—0
Reinforcement Learning-Based Joint Self-Optimisation Method for the Fuzzy Logic Handover Algorithm in 5G HetNets—0
Reinforcement learning based local path planning for mobile robot—0
Reinforcement Learning Based Minimum State-flipped Control for the Reachability of Boolean Control Networks—0
Reinforcement Learning based on Scenario-tree MPC for ASVs—0
Reinforcement Learning based Per-antenna Discrete Power Control for Massive MIMO Systems—0
Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access—0
Reinforcement learning based recommender systems: A survey—0
Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations—0
Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment—0
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