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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 171–180 of 1918 papers

TitleStatusHype
A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging—0
Inverse Q-Learning Done Right: Offline Imitation Learning in Q^π-Realizable MDPsCode0
Distributionally Robust Deep Q-LearningCode0
Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies—0
Reinforcement Learning for Stock Transactions—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
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