Master Thesis: Multi-Agent Reinforcement Learning for Dynamic Climate Policy Games
View the full thesis here. Abstract Despite concerted efforts by researchers and policymakers, governments are failing to implement the global coordination needed to implement policies that could avert the disaster of unmitigated climate change. Existing economic models are often ill-equipped to capture the complexities of dynamic, strategic interactions among multiple agents. The research on international mechanisms such as climate clubs, for instance, is often limited to one-shot games due to the combinatorial explosion of sequential negotiation steps....