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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 681–690 of 1918 papers

TitleStatusHype
Trade-off on Sim2Real Learning: Real-world Learning Faster than Simulations—0
Energy Consumption and Battery Aging Minimization Using a Q-learning Strategy for a Battery/Ultracapacitor Electric Vehicle—0
Balancing Profit, Risk, and Sustainability for Portfolio Management—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
Energy Sharing for Multiple Sensor Nodes with Finite Buffers—0
Enhanced Deep Q-Learning for 2D Self-Driving Cars: Implementation and Evaluation on a Custom Track Environment—0
Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks—0
Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning: Results for the Fighting Game AI Competition—0
Enhancement of High-definition Map Update Service Through Coverage-aware and Reinforcement Learning—0
Exclusively Penalized Q-learning for Offline Reinforcement Learning—0
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