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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 1431–1440 of 1918 papers

TitleStatusHype
Value-of-Information based Arbitration between Model-based and Model-free Control—0
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill DiscoveryCode0
Combining Q-Learning and Search with Amortized Value Estimates—0
Reinforcement Learning with Non-Markovian Rewards—0
A Unified Switching System Perspective and O.D.E. Analysis of Q-Learning Algorithms—0
Learning to Dynamically Coordinate Multi-Robot Teams in Graph Attention Networks—0
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning—0
Neural Temporal-Difference Learning Converges to Global Optima—0
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Propagating Uncertainty in Reinforcement Learning via Wasserstein BarycentersCode0
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