… in the application of reinforcement-learning theory as a … reinforcement-learning and their
arbitration, and consider hierarchical reinforcement-learning schemes and structure learning. …

… mechanisms, and twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. … After that, we discuss important mechanisms for RL, …

… However, the neural mechanisms through which these biases emerge … mechanism. We
aimed to determine whether a computational mechanism summarizing reinforcement learning (…

… reinforcement learning, which was developed in an artificial intelligence community with
intuitions from animal learning … theoretical framework of reinforcement learning and discusses its …

… —what brain mechanisms are … reinforcement learning methods are notorious for not scaling
up to tasks of real-world complexity, our results suggest that selective attention mechanisms …

… reinforcement learning for indirect mechanisms, working with the existing class of sequential
price mechanisms, which generalizes both serial dictatorship and posted price mechanisms …

… based on reiiforcenieiit learning mechanisms that control a … by means of the reinforcement
leomirzg olgorithm. Some … the use of reinforcement learning mechanisms over the Fuzzy…

… First, we develop the first reinforcement learning algorithm that can recover an optimal
dynamic mechanism with no prior knowledge of the problem. In particular, our algorithm is …

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and
computational theories with which we can disentangle psychiatric conditions into the basic …

… Reinforcement learning theory prescribes various algorithms for updating value functions …
We then focus on how reinforcement learning theory can shed new light on the function of the …