In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

The interpretability of an AI agent's behavior is of utmost importance for effective human-AI interaction. To this end, there has been increasing interest in characterizing and generating interpretable behavior of the agent. An alternative approach to guarantee that the agent generates interpretable behavior would be to design the agent's environment such that uninterpretable behaviors are either prohibitively expensive or unavailable to the agent. To date, there has been work under the umbrella of goal or plan recognition design exploring this notion of environment redesign in some specific instances of interpretable of behavior. In this position paper, we scope the landscape of interpretable behavior and environment redesign in all its different flavors. Specifically, we focus on three specific types of interpretable behaviors -- explicability, legibility, and predictability -- and present a general framework for the problem of environment design that can be instantiated to achieve each of the three interpretable behaviors. We also discuss how specific instantiations of this framework correspond to prior works on environment design and identify exciting opportunities for future work.
We present an approach to redesign the environment such that uninterpretable agent behaviors are minimized or eliminated.

The reproducibility of reinforcement-learning research has been highlighted as a key challenge area in the field. In this paper, we present a case study in reproducing the results of one groundbreaking algorithm, AlphaZero, a reinforcement learning system that learns how to play Go at a superhuman level given only the rules of the game. We describe Minigo, a reproduction of the AlphaZero system using publicly available Google Cloud Platform infrastructure and Google Cloud TPUs. The Minigo system includes both the central reinforcement learning loop as well as auxiliary monitoring and evaluation infrastructure. With ten days of training from scratch on 800 Cloud TPUs, Minigo can play evenly against LeelaZero and ELF OpenGo, two of the strongest publicly available Go AIs. We discuss the difficulties of scaling a reinforcement learning system and the monitoring systems required to understand the complex interplay of hyperparameter configurations.
We reproduced AlphaZero on Google Cloud Platform

The field of deep learning has been craving for an optimization method that shows outstanding property for both optimization and generalization.   We propose a method for mathematical optimization based on flows along geodesics, that is, the shortest paths between two points, with respect to the Riemannian metric induced by a non-linear function. In our method, the flows refer to Exponentially Decaying Flows (EDF), as they can be designed to converge on the local solutions exponentially. In this paper, we conduct experiments to show its high performance on optimization benchmarks (i.e., convergence properties), as well as its potential for producing good machine learning benchmarks (i.e., generalization properties).

Introduction of a new optimization method and its application to deep learning.

