We are hoping the aim trainer will also be useful for educational purposes. For example, it is useful for students to practice implementing RL models to solve imitation tasks, and apply learning theory in AI or decision-making problems. The aim trainer is also useful for instructors to use for teaching RL applications in an online setting.
The feature of the online browser-based aim trainer is its simple interface and intuitive design. It is easy for non-technical staff to add new games and set up new environments using a drag and drop game creator interface, or by directly editing the HTML and CSS files.
Currently, the aim trainer is available in English, and is planning to release it in other languages. We also plan to add more features to the task, like recording and exporting game data. As another effort, in the future, we will support other game models like the ones in DARPA’s Integrated Active Learning (IAL) program , which use a pyggan library for generating environments. The pyggan library is a new game generation framework for developing real-time games with Python, which allows us to generate and render interactive 3D environments for visualizing the environments and humans in the game. We are also using the pycairo library for making the environment responsive to the screen resolution.
About the Journal: This paper presents a general-purpose method for model-based offline RL. Using COMBO, we develop a new method for model-based offline RL called COMBO. We demonstrate theoretical guarantees and show how COMBO outperforms both model-free and model-based state-of-the-art methods on a wide variety of benchmark problems. In addition, we develop a new ‘model-free’ offline learning method called COMBO-Inf and a new ‘model-based’ method called COMBO-Inf that uses COMBO to solve a nonlinear inverse problem.
A data collection app like those made by Zendesk (formerly Trystack ) allows for collection of structured data in an offline environment, from surveys to patient medical records. Other examples of data collection apps include Inertia , Flux and Open I . Inertia and F$lux have recently been acquired by the company Intuit .
The Open Platform for Augmented Reality (OPAR) is a free and open source framework for creating augmented reality applications. OPAR allows developers to create and distribute augmented reality applications through a cloud-based web hosting service. Developers can distribute their apps to others without requiring them to install software on their devices. OPAR is designed to work on Android, iOS, iOS®, Windows, and MacOS platforms.
One of the drawbacks to this approach was that we needed to recompile every time we made even a small change to the code. This slowed everything else we were doing down and because we couldn't always have someone waiting for the application to finish building. 827ec27edc