An information theoretic approach to autonomous learning of embodied agents

理想情况下，自主学习系统应该能够收集足够的关于自身及其环境的信息，以解决给定的任务。这明确意味着他们不应该依赖人类监督者。因此，主要的问题是：一个不具备关于自身和环境的先验知识的系统如何识别其任务并学习最佳地解决它，如果它只是偶尔收到一个是-否反馈。这种类型的系统是在未知和动态环境中操作所期望的。为了能够在这样的环境中运行，系统还必须检测干扰，例如一个车轮的堵塞，并找到克服这种障碍的方法。自主学习的能力将变得越来越重要，因为今天的机器人系统的复杂性已经挑战了经典的编程方法。在这个项目中，我们将推导出数学方法，这将有助于实现人工系统中提到的能力。我们主要关注的是理论建设和基础研究。我们认为，在自主学习的背景下，我们必须考虑三个主要方面。首先，学习系统必须有一种内部动机来探索它的身体和环境;探索是这个项目的核心要素。第二，探索必须受到身体和环境的物理约束。不是每一个行动都可以在每一个时间点上进行。自主识别哪些行为是有效的，以及何时将改善学习，因为它大大减少了搜索空间。第三，内部激励必须与外部反馈信号有效结合。在这种情况下，我们认为，体现自主学习领域需要从信息论和信息几何的新方法，使下一个大的一步走向自主。在这个项目中，我们将研究自主学习的理论基础，并在虚拟机器人系统中验证它们。

Ideally, autonomously learning systems should be able to gather enough information about themselves and their environment to solve a given task. This explicitly means that they should not rely on a human supervisor. The main question therefore is: How can a system that ist not equipped with a priori knowledge about itself and the environment identify its task and learn to optimally solve it, if it only occasionally receives a yes-no-feedback. This type of system is desired for operations in unknown and dynamic environments. To be able to function in such environments, the system must also detect disturbances, like a blockage of one wheel, and find a way to overcome such an impairment. The ability of autonomous learning will become more and more essential as the complexity of todays robotic systems already challenge the classical programming approach. In this project, we will derive the mathematical methods which will help to realise the mentioned capacities in artificial systems. Our main concerns are theory construction and basic research. We believe that there are three main aspects that we have to consider in the context of autonomous learning. First, the learning system must have a form of internal motivation to explore its body and environment; exploration is a central element of this project. Second, the exploration must be guided by the physical constraints of the body and environment. Not every action is possible at every point in time. To autonomously recognise which actions are effective and when will improve the learning as it reduces the search space significantly. Third, the internal motivation must be combined effectively with an external feedback signal. In this context, we believe that the field of embodied autonomous learning requires new methods from information theory and information geometry to make the next big step towards autonomy. In this project, we will investigate the theoretical foundations of autonomous learning and validate them in virtual robotic systems.