Sentence examples for a learning agent from inspiring English sources

We present here the Q-learning principles for train rescheduling, which consist of a learning agent and its actions, environment and its states, as well as rewards.

We first present the COA challenge problem and the architecture of Disciple- COA, a learning agent shell from the Disciple family.

This paper discusses a predictive modeling framework actualized in a learning agent that uses logged tutorial interactions to discover predictive characteristics of students.

This paper presents a set of methods by which a learning agent can learn a sequence of increasingly abstract and powerful interfaces to control a robot whose sensorimotor apparatus and environment are initially unknown.

Let Z = {Z0, Z1, Z2, Z3...Z t } be the set of possible states an environment may be in, and A = {a 0,a 1,a2...a t } be a set of actions a learning agent may take.

However, constructing such a learning agent currently requires manual encoding of prior domain knowledge; in addition to being a poor model of human acquisition of prior knowledge, manual knowledge-encoding is both time-consuming and error-prone.

This paper regards it as a problem of finite-time optimization in infinite-horizon control then devises a reinforcement learning agent, termed as the Adaptive Optimal Control (AOC) agent, to carry out the finite-time optimization procedures.

Next, a reinforcement learning agent transforms these paths into trajectories that maintain the bound on the load displacement while following the collision-free path in a timely manner.

A machine learning agent intended to transform aerial images into street maps and back was found to be cheating by hiding information it would need later in "a nearly imperceptible, high-frequency signal".

We present a model of hierarchical curiosity loops for such an autonomous active learning agent, whereby each loop selects the optimal action that maximizes the agent's learning of sensory-motor correlations.

A high-aggressiveness index along with a low fairness index in a TCP source triggers the learning agent to make changes to the TCP parameters and to force the TCP source to be more hospitable towards other flows.