Learning a Latent Representation of the Opponent in Automated Negotiation

Learning a Latent Representation of the Opponent in Automated Negotiation

Gaghi, Radu (TU Delft Electrical Engineering, Mathematics and Computer Science)

Murukannaiah, P.K. (mentor)
Renting, B.M. (mentor)
Zhang, X. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-06-23

This paper introduces a strategy for learning opponent parameters in automated negotiation and using them for future negotiation sessions. The goal is to maximize the agent’s utility while being consistent in its performance over various negotiation scenarios. While a number of reinforcement learning approaches in the field have used Q-learning, this paper uses the newer Proximal Policy Optimization algorithm. Machine learning has been used in opponent modeling, classifying opponents, and learning strategies, but there have been few attempts to store and re-use this information. In an experimental setup, it is shown that this approach outperforms a baseline in terms of individual utility.

Automated negotiation
Machine learning
Reinforcement Learning
Opponent Modelling

http://resolver.tudelft.nl/uuid:376b66f3-09cd-4de3-bb67-7f58b0f86288

Student theses

bachelor thesis

© 2022 Radu Gaghi