Training Reinforcment Learning Agent with Simulation Code

Comprehensive global cooperation is essential to limit global temperature increases while continuing economic development, e.g., reducing severe inequality or achieving long-term economic growth. Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem.\nThe RICE-N IAM is an agent-based model that incorporates DICE climate-economic dynamics and multi-lateral negotiation protocols between several fictitious nations.

Tutorial.ipynb: It provides examples on modifying the code to implement different negotiation protocols. It describes ways of changing the agent observations and action spaces corresponding to the proposed negotiation protocols and implementing the negotiation logic in the provided code. It has a walkthrough of how to train RL agents with the simulation code and how to visualize results from the simulation after running it with a set of agents.

In this notebook two examples of GPU-based training with WarpDrive are presented:

\n
The first example does not include negotiation between regions. Since there is no direct interaction between the different regions without negotiation, total runtime is ~2 minutes.
\n
\n
The second example includes negotiations between regions. These negotiations take place according to the negotiation protocol outlined in rice.py. Total runtime is 15~20 minutes.
\n

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Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem. \nThe RICE-N IAM is an agent-based model that incorporates **DICE climate-economic dynamics** and **multi-lateral negotiation protocols** between several fictitious nations.\n\n**Tutorial.ipynb**: It provides examples on modifying the code to implement different negotiation protocols. It describes ways of changing the agent observations and action spaces corresponding to the proposed negotiation protocols and implementing the negotiation logic in the provided code. It has a walkthrough of how to train RL agents with the simulation code and how to visualize results from the simulation after running it with a set of agents.\n\nIn this notebook two examples of GPU-based training with WarpDrive are presented:\n\n1. The first example does not include negotiation between regions. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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