Contextual Simulated Annealing Q-Learning for Pre-negotiation of Agent-Based Bilateral Negotiations
| Main Authors: | Tiago Pinto, Zita Vale |
|---|---|
| Format: | Proceeding Journal |
| Bahasa: | eng |
| Terbitan: |
, 2019
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/3481578 |
Daftar Isi:
- Electricity markets are complex environments, which have been suffering continuous transformations due to the increase of renewable based generation and the introduction of new players in the system. In this context, players are forced to re-think their behavior and learn how to act in this dynamic environment in order to get as much benefit as possible from market negotiations. This paper introduces a new learning model to enable players identifying the expected prices of future bilateral agreements, as a way to improve the decision-making process in deciding the opponent players to approach for actual negotiations. The proposed model introduces a con-textual dimension in the well-known Q-Learning algorithm, and includes a simulated annealing process to accelerate the convergence process. The proposed model is integrated in a multi-agent decision support system for electricity market players negotiations, enabling the experimentation of results using real data from the Iberian electricity market.
- This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019.