methods are proposed to develop idealized moment-curvature response curves of slender rectangular rc wall sections with uniformly distributed longitudinal reinforcement, using limit states, namely, cracking of concrete in tension, tensile yielding of reinforcement layers, and compression failure of concrete. it is recommended that tensile yielding of an inner layer of reinforcement is considere...

Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This monograph surveys integration of both fields, better known model-based learning. Model-based RL has two main steps: dynamics model planning-learning integration. ...

Over several years, we have developed an approach to spoken dialogue systems that includes rule-based and trainable dialogue managers, spoken language understanding and generation modules, and a comprehensive dialogue system architecture. We present a Reinforcement Learning-based dialogue system that goes beyond standard rule-based models and computes on-line decisions of the best dialogue move...

In this paper, the rib beam bridge slabs were taken as research object. Static load and fatigue tests carried out on benchmark to determine ultimate capacity life of slabs. Then, slab was pre-damaged reinforced with polyurethane concrete. A test study performance. Based damage theory, reinforcement finite element models under different degrees established. The performance systematically studied...

This paper describes some of the interactions of model learning algorithms and planning algorithms we have found in exploring model-based reinforcement learning. The paper focuses on how local trajectory optimizers can be used effectively with learned nonparametric models. We find that trajectory planners that are fully consistent with the learned model often have difficulty finding reasonable ...