Q-learning - Open Access .click

IEEE Transactions on Neural Networks, 2000
This paper develops the theory of quad-Q-learning which is a new learning algorithm that evolved from Q-learning. Quad-Q-learning is applicable to problems that can be solved by "divide and conquer" techniques. Quad-Q-learning concerns an autonomous agent that learns without supervision to act optimally to achieve specified goals.
C, Clausen, H, Wechsler
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Convex Q-Learning

2021 American Control Conference (ACC), 2021
It is well known that the extension of Watkins' algorithm to general function approximation settings is challenging: does the “projected Bellman equation” have a solution? If so, is the solution useful in the sense of generating a good policy? And, if the preceding questions are answered in the affirmative, is the algorithm consistent?
Fan Lu +3 more
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Neural Q-learning

Neural Computing & Applications, 2003
In this paper we introduce a novel neural reinforcement learning method. Unlike existing methods, our approach does not need a model of the system and can be trained directly using the measurements of the system. We achieve this by only using one function approximator and approximate the improved policy from this.
Stephan ten Hagen, Ben Kr�se
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CVaR Q-Learning

2021
In this paper we focus on reinforcement learning algorithms that are sensitive to risk. The notion of risk we work with is the well-known conditional value-at-risk (CVaR). We describe a faster method for computing value iteration updates for CVaR markov decision processes (MDP). This improvement then opens doors for a sampling version of the algorithm,
Silvestr Stanko, Karel Macek
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reinforcement learning
fos: computer and information sciences
machine learning cs.lg

computer science - machine learning
artificial intelligence cs.ai
computer science - artificial intelligence

deep reinforcement learning
machine learning stat.ml
statistics - machine learning