Learning to Teach in Cooperative Multiagent Reinforcement Learning
We present a framework and algorithm for peer-to-peer teaching in cooperative multiagent reinforcement learning. Our algorithm, Learning to Coordinate and Teach Reinforcement (LeCTR), trains advising policies by using students' learning progress as a teaching reward. Agents using LeCTR learn to assume the role of a teacher or student at the appropriate moments, exchanging action advice to accelerate the entire learning process. Our algorithm supports teaching heterogeneous teammates, advising under communication constraints, and learns both what and when to advise. LeCTR is demonstrated to outperform the final performance and rate of learning of prior teaching methods on multiple benchmark domains. To our knowledge, this is the first approach for learning to teach in a multiagent setting.
Authors

Are you an author of this paper? Check the Twitter handle we have for you is correct.

Shayegan Omidshafiei (add twitter)
Dong-Ki Kim (add twitter)
Miao Liu (add twitter)
Gerald Tesauro (add twitter)
Matthew Riemer (add twitter)
Christopher Amato (add twitter)
Murray Campbell (add twitter)
Jonathan P. How (add twitter)
Ask The Authors

Ask the authors of this paper a question or leave a comment.

Read it. Rate it.
#1. Which part of the paper did you read?

#2. The paper contains new data or analyses that is openly accessible?
#3. The conclusion is supported by the data and analyses?
#4. The conclusion is of scientific interest?
#5. The result is likely to lead to future research?

Github
User:
None (add)
Repo:
None (add)
Stargazers:
0
Forks:
0
Open Issues:
0
Network:
0
Subscribers:
0
Language:
None
Youtube
Link:
None (add)
Views:
0
Likes:
0
Dislikes:
0
Favorites:
0
Comments:
0
Other
Sample Sizes (N=):
Inserted:
Words Total:
Words Unique:
Source:
Abstract:
None
06/26/18 06:13PM
8,359
2,565
Tweets
cbtheis: Learning to Teach in Cooperative Multiagent Reinforcement Learning https://t.co/ucEJVuTsUP @IBMResearch
Images
Related