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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 341–350 of 1918 papers

TitleStatusHype
Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks—0
Cell Switching in HAPS-Aided Networking: How the Obscurity of Traffic Loads Affects the Decision—0
Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning—0
Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources using Autonomous Surface Vehicles—0
Challenging On Car Racing Problem from OpenAI gym—0
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach—0
Characterizing the Action-Generalization Gap in Deep Q-Learning—0
Chemoreception and chemotaxis of a three-sphere swimmer—0
Chrome Dino Run using Reinforcement Learning—0
Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach—0
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