Carbon-Footprint-Multi-Agent-Reinforcement-Learning
Reducing Global Carbon Footprint based on Multi-Agent Reinforcement Learning - School of AI Fellowship Research
This research paper intends to model the investment of groups of countries into carbon emission reductions based on a Mixed Markov Game setting, applying the principles of off-policy single-agent Reinforcement Learning to a multi-agent setting with a Markov Decision Process (MDP). The study shows that countries which are choosing their carbon emission reduction actions under the constraint of optimizing their and their partners’ mid-term economic benefit, achieve both higher cumulative rewards and higher reductions in their per capita CO2 consumption, than their counterparts.
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https://meshkore.com/agent/valdinious-carbon-footprint-multi-agent-reinforcement-learningFor machines — the raw two-step (resolve → call directly)
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# We never proxy the call.
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