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Data-Efficient Scalable Reinforcement Learning for Practical Robotic Environments

Data-Efficient Scalable Reinforcement Learning for Practical Robotic Environments

Objective

The robotics industry is in the process of greater adoption of machine learning. Recent reinforcement learning (RL) and AI breakthroughs, such as AlphaGo, rely on collecting large amounts of data. Such methods are unsuitable for real robots which often can only afford a few trials. Moreover, some states are unsafe to explore, e.g. running over a cliff. Conversely, works such as PILCO combine Bayesian models with model-based RL to improve data efficiency. Those frameworks typically thrive in small data regimes. The goal of this project is to develop RL algorithms that scale to high dimensions while learning with less data. The main pillars of our methodology are RL, recurrent networks, Bayesian methods, embodied exploration, and optimization. To tackle the data efficiency, we adopt model-based RL approaches. We plan to combine representation learning and dynamics in a single model, leading to high predictive power and low-dimensional internal state spaces. Notably, we use methods that can learn disentangled representations, e.g. infoGAN. In practical robots, effective exploration is a real problem in current approaches. We want to leverage recent works in embodied exploration by the host group which allows various real-world robots to explore their capabilities in minutes of interaction. I received my Ph.D. for work in optimization with Dr. William Hager. I also conducted postdoctoral research in machine learning. The Autonomous Learning group is led by Dr. Georg Martius, who has previously studied artificial intrinsic motivation, the self-organized exploration of sensorimotor coordination via information theory, and internal model learning. He also developed the robotics environment LPZRobots. I will gain extensive experience in practical robotics, embodied exploration, and information theory through the collaboration and mature as an advanced AI researcher. Both Dr. Martius and I have a track record of publishing code online. We will continue this effort.
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Coordinator

MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV

Address

Hofgartenstrasse 8
80539 Munich

Germany

Activity type

Other

EU Contribution

€ 159 460,80

Project information

Grant agreement ID: 798321

Status

Ongoing project

  • Start date

    1 April 2018

  • End date

    31 March 2020

Funded under:

H2020-EU.1.3.2.

  • Overall budget:

    € 159 460,80

  • EU contribution

    € 159 460,80

Coordinated by:

MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV

Germany