3,140 research outputs found
Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression
Developing mathematical models of dynamic systems is central to many
disciplines of engineering and science. Models facilitate simulations, analysis
of the system's behavior, decision making and design of automatic control
algorithms. Even inherently model-free control techniques such as reinforcement
learning (RL) have been shown to benefit from the use of models, typically
learned online. Any model construction method must address the tradeoff between
the accuracy of the model and its complexity, which is difficult to strike. In
this paper, we propose to employ symbolic regression (SR) to construct
parsimonious process models described by analytic equations. We have equipped
our method with two different state-of-the-art SR algorithms which
automatically search for equations that fit the measured data: Single Node
Genetic Programming (SNGP) and Multi-Gene Genetic Programming (MGGP). In
addition to the standard problem formulation in the state-space domain, we show
how the method can also be applied to input-output models of the NARX
(nonlinear autoregressive with exogenous input) type. We present the approach
on three simulated examples with up to 14-dimensional state space: an inverted
pendulum, a mobile robot, and a bipedal walking robot. A comparison with deep
neural networks and local linear regression shows that SR in most cases
outperforms these commonly used alternative methods. We demonstrate on a real
pendulum system that the analytic model found enables a RL controller to
successfully perform the swing-up task, based on a model constructed from only
100 data samples
Regression between headmaster leadership, task load and job satisfaction of special education integration program teacher
Managing school is a daunting task for a headmaster. This responsibility is exacerbated when it involves the Special Education Integration Program (SEIP). This situation requires appropriate and effective leadership in addressing some of the issues that are currently taking place at SEIP such as task load and job satisfaction. This study aimed to identify the influence of headmaster leadership on task load and teacher job satisfaction at SEIP. This quantitative study was conducted by distributing 400 sets of randomized questionnaires to SEIP teachers across Malaysia through google form. The data obtained were then analyzed using Structural Equation Modeling (SEM) and AMOS software. The results show that there is a significant positive effect on the leadership of the headmaster and the task load of the teacher. Likewise, the construct of task load and teacher job satisfaction has a significant positive effect. However, for the construct of headmaster leadership and teacher job satisfaction, there was no significant positive relationship. This finding is very important as a reference to the school administration re-evaluating their leadership so as not to burden SEIP teachers and to give them job satisfaction. In addition, the findings of this study can also serve as a guide for SEIP teachers to increase awareness of the importance of managing their tasks. This study also focused on education leadership in general and more specifically on special education leadership
Benchmarking Deep Reinforcement Learning for Continuous Control
Recently, researchers have made significant progress combining the advances
in deep learning for learning feature representations with reinforcement
learning. Some notable examples include training agents to play Atari games
based on raw pixel data and to acquire advanced manipulation skills using raw
sensory inputs. However, it has been difficult to quantify progress in the
domain of continuous control due to the lack of a commonly adopted benchmark.
In this work, we present a benchmark suite of continuous control tasks,
including classic tasks like cart-pole swing-up, tasks with very high state and
action dimensionality such as 3D humanoid locomotion, tasks with partial
observations, and tasks with hierarchical structure. We report novel findings
based on the systematic evaluation of a range of implemented reinforcement
learning algorithms. Both the benchmark and reference implementations are
released at https://github.com/rllab/rllab in order to facilitate experimental
reproducibility and to encourage adoption by other researchers.Comment: 14 pages, ICML 201
- …