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Home Authors Posts by Jiale Zhi

Jiale Zhi

Jiale Zhi
Jiale Zhi is a senior software engineer with Uber AI. His area of interest is distributed computing, big data, scientific computation, evolutionary computing, and reinforcement learning. He is also interested in real-world applications of machine learning in traditional software engineering. He is the creator of the Fiber project, a scalable, distributed framework for large scale parallel computation applications. Before Uber AI, he was a Tech Lead in Uber's edge team, which manages Uber's global mobile network traffic and routing.

Engineering Blog Articles

Fiber: Distributed Computing for AI Made Simple

Project Homepage: GitHub

Over the past several years, increasing processing power of computing machines has led to an increase in machine learning advances. More and more, algorithms exploit parallelism and rely on distributed training to process an enormous amount of

Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions


Jeff Clune and Kenneth Stanley were co-senior authors on this work and our associated research paper.

Machine learning (ML) powers many technologies and services that underpin Uber’s platforms, and we invest in advancing fundamental ML research and engaging with

Research Papers

Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods

J. Zhi, R. Wang, J. Clune, K. Stanley
Recent advances in machine learning are consistently enabled by increasing amounts of computation. Reinforcement learning (RL) and population-based methods in particular pose unique challenges for efficiency and flexibility to the underlying distributed computing frameworks. These challenges include frequent interaction with simulations, the need for dynamic scaling, and the need for a user interface with low adoption cost and consistency across different backends. In this paper we address these challenges while still retaining development efficiency and flexibility for both research and practical applications by introducing Fiber, a scalable distributed computing framework for RL and population-based methods. [...] [PDF]

Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

R. Wang, J. Lehman, A. Rawal, J. Zhi, Y. Li, J. Clune, K. Stanley
Creating open-ended algorithms, which generate their own never-ending stream of novel and appropriately challenging learning opportunities, could help to automate and accelerate progress in machine learning. A recent step in this direction is the Paired Open-Ended Trailblazer (POET), an algorithm that generates and solves its own challenges, and allows solutions to goal-switch between challenges to avoid local optima. Here we introduce and empirically validate two new innovations to the original algorithm, as well as two external innovations designed to help elucidate its full potential. [...] [PDF]
International Conference on Machine Learning (ICML), 2020

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