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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 21–30 of 1918 papers

TitleStatusHype
Reinforcement Learning for Stock Transactions—0
Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies—0
OPA-Pack: Object-Property-Aware Robotic Bin Packing—0
When a Reinforcement Learning Agent Encounters Unknown Unknowns—0
Imagination-Limited Q-Learning for Offline Reinforcement Learning—0
Automatic Reward Shaping from Confounded Offline Data—0
ShiQ: Bringing back Bellman to LLMs—0
Bias or Optimality? Disentangling Bayesian Inference and Learning Biases in Human Decision-Making—0
Convert Language Model into a Value-based Strategic Planner—0
A Large Language Model-Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Windows—0
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