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  • PhD Thesis Proposal - Aleksei Petrenko

    Thu, May 05, 2022 @ 02:00 PM - 04:00 PM

    Computer Science

    University Calendar


    Date: 05/05/22 (Thursday)

    Time: 2pm
    Physical location: RTH 406 conference room

    Zoom URL: https://usc.zoom.us/j/8712894950

    Committee:
    - Gaurav Sukhatme
    - Rahul Jain
    - Jesse Thomason
    - Mike Zyda
    - Stefanos Nikolaidis


    Abstract:
    We propose accelerated methods for deep reinforcement learning that enable state-of-the-art large scale experiments with simulated environments on limited hardware. We break down performance bottlenecks of reinforcement learning and discuss optimization techniques such as asynchronous experience collection for heterogeneous learning systems, large-batch rendering for high throughput simulation, and end-to-end training systems design that leverages fast GPU-based simulators. The proposal includes a case study of multiple reinforcement learning projects which heavily rely on accelerated training: from agents that learn how to execute instructions spoken in natural language to quadrotor drones trained in a simulated environment and deployed in the real world.






    Location: Ronald Tutor Hall of Engineering (RTH) - 306

    WebCast Link: https://usc.zoom.us/j/8712894950

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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