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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 1471–1480 of 1918 papers

TitleStatusHype
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central InferenceCode0
On the Reduction of Variance and Overestimation of Deep Q-Learning—0
Zap Q-Learning With Nonlinear Function Approximation—0
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments—0
A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning—0
Tactical Reward Shaping: Bypassing Reinforcement Learning with Strategy-Based Goals—0
Toward Synergic Learning for Autonomous Manipulation of Deformable Tissues via Surgical Robots: An Approximate Q-Learning Approach—0
Combining No-regret and Q-learningCode0
Reinforcement Learning with Structured Hierarchical Grammar Representations of Actions—0
I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action—0
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