# Multi-Agent Reinforcement Learning for Greenhouse Gas Offset Credit Markets

arXiv: https://arxiv.org/abs/2504.11258 · HTML: https://mlsanity.com/paper/2504.11258/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Continuous Deep Q-Learning with Model-based Acceleration](https://mlsanity.com/paper/1603.00748/) (2016)
- [Deep Q-Learning for Nash Equilibria: Nash-DQN](https://mlsanity.com/paper/1904.10554/) (2019)
- [VA-learning as a more efficient alternative to Q-learning](https://mlsanity.com/paper/2305.18161/) (2023)
- [Deep Learning for Principal-Agent Mean Field Games](https://mlsanity.com/paper/2110.01127/) (2021)
- [Mean Field Models to Regulate Carbon Emissions in Electricity Production](https://mlsanity.com/paper/2102.09434/) (2021)
- [A Mean-Field Game Approach to Equilibrium Pricing in Solar Renewable Energy Certificate Markets](https://mlsanity.com/paper/2003.04938/) (2020)
- [A groundwater market model](https://mlsanity.com/paper/2501.14071/) (2025)

## Built on by (most cited first, 1 of 1)

- [Learning Principal-Agent Contracts for Equitable Smallholder Carbon Farming under Moral Hazard and Adverse Selection](https://mlsanity.com/paper/2609.20404/) (2026)

## Abstract (arXiv)

Climate change is a major threat to the future of humanity, and its impacts are being intensified by excess man-made greenhouse gas emissions. One method governments can employ to control these emissions is to provide firms with emission limits and penalize any excess emissions above the limit. Excess emissions may also be offset by firms who choose to invest in carbon reducing and capturing projects. These projects generate offset credits which can be submitted to a regulating agency to offset a firm's excess emissions, or they can be traded with other firms. In this work, we characterize the finite-agent Nash equilibrium for offset credit markets. As computing Nash equilibria is an NP-hard problem, we utilize the modern reinforcement learning technique Nash-DQN to efficiently estimate the market's Nash equilibria. We demonstrate not only the validity of employing reinforcement learning methods applied to climate themed financial markets, but also the significant financial savings emitting firms may achieve when abiding by the Nash equilibria through numerical experiments.