Cooperative Inverse RL

Add to your list(s) Download to your calendar using vCal

• Robert Pinsler; Adria Garriga Alonso
• Thursday 26 October 2017, 13:30-15:00
• Engineering Department, CBL Seminar Room 4-38.

If you have a question about this talk, please contact Alessandro Davide Ialongo.

Abstract:

The value alignment problem consists in ensuring the values of an AI system align with the values of its operator. A potential solution to this problem is formalised as the Inverse Reinforcement Learning (IRL) setting. In IRL , the goal is to infer the reward function of an agent (a human), just from observing its behaviour in the environment. In Cooperative IRL , the agents are allowed to interact. From this, more effective teaching strategies than passive observation emerge. We will talk about formalising this problem, and an algorithm to approximate good teaching strategies.

Recommended reading:

Main Paper:

• Cooperative Inverse Reinforcement Learning: https://papers.nips.cc/paper/6420-cooperative-inverse-reinforcement-learning.pdf

Optional background reading on Inverse Reinforcement Learning:

• Apprenticeship Learning via Inverse Reinforcement Learning: http://ai.stanford.edu/~ang/papers/icml04-apprentice.pdf
• Maximum Entropy Inverse Reinforcement Learning: https://www.aaai.org/Papers/AAAI/2008/AAAI08-227.pdf

This talk is part of the Machine Learning Reading Group @ CUED series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge University Engineering Department Talks
• Cambridge talks
• Centre for Smart Infrastructure & Construction
• Chris Davis' list
• Computational Continuum Mechanics Group Seminars
• Engineering Department, CBL Seminar Room 4-38
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group Journal Clubs
• Inference Group Summary
• Information Engineering Division seminar list
• Interested Talks
• ML
• Machine Learning Reading Group
• Machine Learning Reading Group @ CUED
• Machine Learning Summary
• Quantum Matter Journal Club
• Required lists for MLG
• School of Technology
• Simon Baker's List
• TQS Journal Clubs
• Trust & Technology Initiative - interesting events
• bld31
• custom
• ndk22's list
• ob366-ai4er
• rp587
• yk373's list

Note that ex-directory lists are not shown.