This paper provides a new deterministic Q-learning with a presumed knowledge about the
distance from the current state to both the next state and the goal. This knowledge is …

It is a fundamental question how our brain performs a given motor task in a real-time fashion
with the slow sensorimotor system. Computational theory proposed an influential idea of …

Background To validate the use of artificial neural network (ANN) models for predicting 5-
year mortality in HCC and to compare their predictive capability with that of logistic …

Temporal-difference (TD) learning methods are widely used in reinforcement learning to
estimate the expected return for each state, without a model, because of their significant …

Navigating multiple tasks $\unicode {x2014} $ for instance in succession as in continual or
lifelong learning, or in distributions as in meta or multi-task learning $\unicode {x2014} …

This paper proposes a novel action selection method based on quantum computation and
reinforcement learning (RL). Inspired by the advantages of quantum computation, the …

Effective life-long deployment of an autonomous agent in a complex environment demands
that the agent has some model of itself and its environment. Such models are inherently …

Many sequential decision-making systems leverage data collected using prior policies to
propose a new policy. For critical applications, it is important that high-confidence …

The dilemma between exploration and exploitation is an important topic in reinforcement
learning (RL). Most successful approaches in addressing this problem tend to use some …

Research in artificial general intelligence aims to create agents that can learn from their own
experience to solve arbitrary tasks in complex and dynamic settings. To do so effectively and …