AI4GCC -- Track 3: Consumption and the Challenges of Multi-Agent RL
- URL: http://arxiv.org/abs/2308.05260v1
- Date: Wed, 9 Aug 2023 23:52:41 GMT
- Title: AI4GCC -- Track 3: Consumption and the Challenges of Multi-Agent RL
- Authors: Marco Jiralerspong, Gauthier Gidel
- Abstract summary: We highlight two potential areas for improvement that could enhance the competition's ability to identify and evaluate proposed negotiation protocols.
Firstly, we suggest the inclusion of an additional index that accounts for consumption/utility as part of the evaluation criteria.
Secondly, we recommend further investigation into the learning dynamics of agents in the simulator and the game theoretic properties of outcomes from proposed negotiation protocols.
- Score: 14.046451550358427
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The AI4GCC competition presents a bold step forward in the direction of
integrating machine learning with traditional economic policy analysis. Below,
we highlight two potential areas for improvement that could enhance the
competition's ability to identify and evaluate proposed negotiation protocols.
Firstly, we suggest the inclusion of an additional index that accounts for
consumption/utility as part of the evaluation criteria. Secondly, we recommend
further investigation into the learning dynamics of agents in the simulator and
the game theoretic properties of outcomes from proposed negotiation protocols.
We hope that these suggestions can be of use for future iterations of the
competition/simulation.
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