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Teaching Multiple Inverse Reinforcement Learners.

Francisco S Melo¹, Manuel Lopes¹

¹INESC-ID, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal.

Frontiers in Artificial Intelligence

|October 4, 2021

Summary

This summary is machine-generated.

We developed machine teaching algorithms for multiple inverse reinforcement learners facing heterogeneous tasks. Our findings show a single demonstration may not suffice for diverse agents, necessitating tailored teaching strategies like SplitTeach or JointTeach.

Keywords:

Markov decision processes class teaching heterogeneous multi-agent teaching inverse reinforcement learning optimal teaching

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Area of Science:

Artificial Intelligence
Machine Learning
Reinforcement Learning

Background:

Machine teaching aims to efficiently impart knowledge to artificial agents.
Inverse reinforcement learning (IRL) infers reward functions from expert demonstrations.
Teaching heterogeneous learners presents unique challenges due to differing capabilities.

Purpose of the Study:

To formalize the problem of optimally teaching sequential tasks to a heterogeneous class of multiple inverse reinforcement learners.
To analyze the conditions under which a single demonstration can be used for teaching diverse agents.
To propose novel machine teaching algorithms addressing the challenges of heterogeneous IRL.

Main Methods:

Formalization of the optimal teaching problem for heterogeneous IRL.
Theoretical analysis to identify conditions for single-demonstration teaching.
Development of two algorithms: SplitTeach (individualized teaching after group learning) and JointTeach (single demonstration for the whole class).

Main Results:

Teaching heterogeneous learners with a single demonstration is often not possible due to agent differences.
SplitTeach ensures perfect task acquisition for all learners but requires more teaching effort.
JointTeach minimizes teaching effort but may not guarantee perfect task recovery.

Conclusions:

Heterogeneous student classes pose significant challenges for single-demonstration machine teaching.
SplitTeach offers optimal teaching performance, while JointTeach prioritizes minimal teaching effort.
The proposed algorithms and theoretical analysis provide valuable insights into multi-agent machine teaching.