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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 1171–1180 of 1918 papers

TitleStatusHype
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—0
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles—0
Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning—0
Reinforcement Learning-based Joint Path and Energy Optimization of Cellular-Connected Unmanned Aerial Vehicles—0
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation—0
Solving The Lunar Lander Problem under Uncertainty using Reinforcement LearningCode0
Learning Principle of Least Action with Reinforcement LearningCode0
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment—0
Provable Multi-Objective Reinforcement Learning with Generative Models—0
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