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Program Embeddings for Rapid Mechanism Evaluation.
Sai Kiran Narayanaswami, David Fridovich-Keil, Swarat Chaudhuri,
and Peter Stone.
In ICRA Workshop on Multi-Robot Learning, May 2023.
[PDF]1.3MB [poster.pdf]916.2kB
Mechanisms such as auctions, voting and traffic control systems incentivize agents in multiagent systems in order to achieve or optimize certain global outcomes such as safety, productivity, and welfare. Designing mechanisms in the form of programs would bring several benefits such as interpretability, transparency and verifiability. However, program synthesis for finding suitable mechanisms from a search space represented by programs leads to computationally expensive two-level optimization problems, where mechanisms need to be evaluated in an inner loop, which entails learning agent responses for each mechanism. Multi-Task Learning (MTL) approaches efficiently learn parameterized agent strategies that can be used to act in various tasks, here mechanisms defined by programs. Such multi-task agent policies allow for rapid evaluation of mechanism performance for a given program by bypassing the need to learn agent responses from scratch, thus allowing for efficient evaluation of mechanisms. In this work, we use learned program representations embeddings to provide suitable task contexts that make MTL feasible with combinatorially large programmatic search spaces of mechanisms. We demonstrate experimentally that program embeddings generated by an off-the-shelf Code2Vec model are sufficient for reconstructing matrix games based on only a programmatic description. The embeddings are also able to serve as task context to guide a set of agent strategies to act near-optimally in mechanisms from the search space.
@InProceedings{nskiran_ICRA2023WMRL,
author = {Sai Kiran Narayanaswami and David Fridovich-Keil and Swarat Chaudhuri and Peter Stone},
title = {Program Embeddings for Rapid Mechanism Evaluation},
booktitle = {ICRA Workshop on Multi-Robot Learning},
year = {2023},
month = {May},
location = {London, UK},
abstract = {Mechanisms such as auctions, voting and traffic control systems incentivize agents in multiagent systems in order to achieve or optimize certain global outcomes such as safety, productivity, and welfare. Designing mechanisms in the form of programs would bring several benefits such as interpretability, transparency and verifiability. However, program synthesis for finding suitable mechanisms from a search space represented by programs leads to computationally expensive two-level optimization problems, where mechanisms need to be evaluated in an inner loop, which entails learning agent responses for each mechanism. Multi-Task Learning (MTL) approaches efficiently learn parameterized agent strategies that can be used to act in various tasks, here mechanisms defined by programs. Such multi-task agent policies allow for rapid evaluation of mechanism performance for a given program by bypassing the need to learn agent responses from scratch, thus allowing for efficient evaluation of mechanisms. In this work, we use learned program representations embeddings to provide suitable task contexts that make MTL feasible with combinatorially large programmatic search spaces of mechanisms. We demonstrate experimentally that program embeddings generated by an off-the-shelf Code2Vec model are sufficient for reconstructing matrix games based on only a programmatic description. The embeddings are also able to serve as task context to guide a set of agent strategies to act near-optimally in mechanisms from the search space.},
}
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