Tutorial: Tools for Robotic Reinforcement Learning

Abstract

Reinforcement learning (RL) methods have received much attention due to impressive results in many robotic applications. While RL promises learning-based control of near-optimal behaviors in theory, successful learning can elude practitioners due to various implementation challenges. Even if the best-suited learning method was selected, learning performance can nonetheless disappoint due to badly chosen hyper-parameters or an unreliable implementation of the algorithm. Furthermore, a learning task can be made unnecessarily hard by incorrect specifications. This full-day tutorial points-out these practical pitfalls and introduces the audience to the tools for robotic RL that will aid roboticists in successfully solving robotic learning tasks, both in simulation and the real-world.

Date
May 23, 2022 08:00 — 18:00
Location
Online / Philadelphia
Avatar
Antonin Raffin
Research Engineer in Robotics and Machine Learning

Robots. Machine Learning. Blues Dance.

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