EU Call for Applications: NGI Explorers Program in USA - Verifiable Reinforcement Learning

Reinforcement learning and sequential decision-making have been revolutionized in recent years thanks to advancements in deep neural networks. One of the most recent breakthroughs was accomplished by the AlphaGo system and its victory over the world Go champion.
Applications are closed

NGI Explorers Program is looking for Europe's Top researchers and innovators in emerging Internet technologies to join a unique opportunity in the US.

3-6 MONTH EXPEDITION TO THE US

Work directly with a US partner to accelerate your idea.

100% SPONSORSHIP PLAN

Financial support will be 100% sponsored with EU public grants.

MENTORSHIP PROGRAM

A coach will give direct support throughout the expedition.

GOALS
 
Reinforcement learning and sequential decision-making have been revolutionized in recent years thanks to advancements in deep neural networks. One of the most recent breakthroughs was accomplished by the AlphaGo system and its victory over the world Go champion. However, even in this impressive system, the learned agent performed sub-optimal actions that puzzled both the Go and the reinforcement learning communities.
 
Such failures in decision-making motivate the need for methods that can provide (statistical) guarantees on the actions performed by an agent. We are interested in establishing such guarantees in both discrete and continuous systems where agents learn policies, or action plans, through experience by interacting with their environment.
 
DETAILS
 
Some problems of interest in this domain include, but are not limited to the following:
  • Decision-making in partially observable Markov Decision Processes.
  • Satisfying probabilistic guarantees on the behavior of a learned agent when approximate value functions (i.e. neural networks) are used to measure utility.
  • Control of hybrid systems resulting from the discretization of continuous space induced by a given set of behavioral specifications. Such specifications are typically defined by a temporal logic such as computation tree logic and linear temporal logic.
  • Decision-making in adversarial stochastic games.
  • Reinforcement learning as a constrained optimization problem wherein expected long-term rewards are to be maximized while satisfying bounds on the probabilities of satisfying various behavioral specifications.
 

SKILLS REQUIRED

A basic understanding of Reinforcement Learning.

Application Deadline: 31 July 2019
 

Source: NGI Explorers Program

Illustration Photo: SISYPHUS is a robot that learns to crawl using a simple AI algorithm called reinforcement learning. The robot tries random actions at first and learns if it is moving forward or backward. Over time it connects actions that move it forward. (credits: mangtronix / Flickr Creative Commons Attribution-ShareAlike 2.0 Generic (CC BY-SA 2.0))

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Adalidda's Team

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