Achieving Diverse Objectives with AI-driven Prices in Deep Reinforcement
Learning Multi-agent Markets
- URL: http://arxiv.org/abs/2106.06060v1
- Date: Thu, 10 Jun 2021 21:26:17 GMT
- Title: Achieving Diverse Objectives with AI-driven Prices in Deep Reinforcement
Learning Multi-agent Markets
- Authors: Panayiotis Danassis, Aris Filos-Ratsikas, Boi Faltings
- Abstract summary: We propose a practical approach to computing market prices and allocations via a deep reinforcement learning policymaker agent.
Our policymaker is much more flexible, allowing us to tune the prices with regard to diverse objectives.
As a highlight of our findings, our policymaker is significantly more successful in maintaining resource sustainability.
- Score: 35.02265584959417
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a practical approach to computing market prices and allocations
via a deep reinforcement learning policymaker agent, operating in an
environment of other learning agents. Compared to the idealized market
equilibrium outcome -- which we use as a benchmark -- our policymaker is much
more flexible, allowing us to tune the prices with regard to diverse objectives
such as sustainability and resource wastefulness, fairness, buyers' and
sellers' welfare, etc. To evaluate our approach, we design a realistic market
with multiple and diverse buyers and sellers. Additionally, the sellers, which
are deep learning agents themselves, compete for resources in a common-pool
appropriation environment based on bio-economic models of commercial fisheries.
We demonstrate that: (a) The introduced policymaker is able to achieve
comparable performance to the market equilibrium, showcasing the potential of
such approaches in markets where the equilibrium prices can not be efficiently
computed. (b) Our policymaker can notably outperform the equilibrium solution
on certain metrics, while at the same time maintaining comparable performance
for the remaining ones. (c) As a highlight of our findings, our policymaker is
significantly more successful in maintaining resource sustainability, compared
to the market outcome, in scarce resource environments.
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