Deep reinforcement learning in World-Earth system models to discover sustainable management strategies
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Wolfram Barfuss | Jonathan F. Donges | Jobst Heitzig | Felix M. Strnad | J. Heitzig | W. Barfuss | J. Donges | Felix M. Strnad
[1] Sergey Levine,et al. End-to-End Training of Deep Visuomotor Policies , 2015, J. Mach. Learn. Res..
[2] J. Heitzig,et al. A Thought Experiment on Sustainable Management of the Earth System , 2018, Sustainability.
[3] Emilie Lindkvist,et al. Strategies for sustainable management of renewable resources during environmental change , 2017, Proceedings of the Royal Society B: Biological Sciences.
[4] M. Scheffer,et al. Trajectories of the Earth System in the Anthropocene , 2018, Proceedings of the National Academy of Sciences.
[5] Wolfgang Lucht,et al. Closing the loop: Reconnecting human dynamics to Earth System science , 2017 .
[6] D. L. Kelly,et al. Integrated Assessment Models For Climate Change Control∗ , 1998 .
[7] Guillaume Deffuant,et al. Viability and Resilience of Complex Systems , 2011 .
[8] Liang Wang,et al. Climate modification directed by control theory , 2008, ArXiv.
[9] J. Kurths,et al. When optimization for governing human-environment tipping elements is neither sustainable nor safe , 2018, Nature Communications.
[10] Wolfgang Lucht,et al. Macroscopic description of complex adaptive networks coevolving with dynamic node states. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.
[11] J. Norberg,et al. Modeling experiential learning: The challenges posed by threshold dynamics for sustainable renewable resource management , 2014 .
[12] Claudia Pahl-Wostl,et al. Models at the interface between science and society: impacts and options , 2000 .
[13] Chris Watkins,et al. Learning from delayed rewards , 1989 .
[14] Yuval Tassa,et al. Continuous control with deep reinforcement learning , 2015, ICLR.
[15] Jonathan F. Donges,et al. Topology of sustainable management of dynamical systems with desirable states : from defining planetary boundaries to safe operating spaces in the Earth system , 2015 .
[16] Arslan Munir,et al. Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger , 2017, ArXiv.
[17] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[18] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[19] Marco Wiering,et al. Reinforcement Learning , 2014, Adaptation, Learning, and Optimization.
[20] Jonathan F. Donges,et al. Towards representing human behavior and decision making in Earth system models – an overview of techniques and approaches , 2017 .
[21] J. Heitzig,et al. Self-enforcing strategies to deter free-riding in the climate change mitigation game and other repeated public good games , 2011, Proceedings of the National Academy of Sciences.
[22] Razvan Pascanu,et al. Learning to Navigate in Complex Environments , 2016, ICLR.
[23] Wolfgang Lucht,et al. Tipping elements in the Earth's climate system , 2008, Proceedings of the National Academy of Sciences.
[24] von der Osten,et al. Intelligent decision-making in coupled socio-ecological systems , 2017 .
[25] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[26] Sergey Levine,et al. Temporal Difference Models: Model-Free Deep RL for Model-Based Control , 2018, ICLR.
[27] Yuxi Li,et al. Deep Reinforcement Learning , 2018, Reinforcement Learning for Cyber-Physical Systems.
[28] S. Carpenter,et al. Planetary boundaries: Guiding human development on a changing planet , 2015, Science.
[29] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[30] Demis Hassabis,et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play , 2018, Science.
[31] I. C. Prentice,et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .
[32] Gerald Tesauro,et al. Temporal difference learning and TD-Gammon , 1995, CACM.
[33] H. Schellnhuber. Tipping elements in the Earth System , 2009, Proceedings of the National Academy of Sciences.
[34] R. Pindyck. The Use and Misuse of Models for Climate Policy , 2015, Review of Environmental Economics and Policy.
[35] F. Chapin,et al. A safe operating space for humanity , 2009, Nature.
[36] W. Brian Arthur,et al. On designing economic agents that behave like human agents , 1993 .
[37] Sandy H. Huang,et al. Adversarial Attacks on Neural Network Policies , 2017, ICLR.
[38] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[39] Wolfgang Lucht,et al. Sustainable use of renewable resources in a stylized social–ecological network model under heterogeneous resource distribution , 2016 .
[40] H. J. Schellnhuber,et al. ‘Earth system’ analysis and the second Copernican revolution , 1999, Nature.
[41] Cezar Ionescu,et al. The impact of uncertainty on optimal emission policies , 2017 .
[42] Wolfgang Lucht,et al. Earth system modelling with complex dynamic human societies: the copan:CORE World-Earth modeling framework , 2018 .
[43] Will Steffen,et al. The topology of non-linear global carbon dynamics: from tipping points to planetary boundaries , 2013 .
[44] Richard N. Zare,et al. Optimizing Chemical Reactions with Deep Reinforcement Learning , 2017, ACS central science.
[45] Kate Raworth,et al. A Safe and Just Space for Humanity: Can we live within the doughnut? , 2012 .
[46] Ulrich Parlitz,et al. Sustainability, collapse and oscillations in a simple World-Earth model , 2017 .
[47] John Schulman,et al. Concrete Problems in AI Safety , 2016, ArXiv.
[48] Jürgen Kurths,et al. Deterministic limit of temporal difference reinforcement learning for stochastic games , 2018, Physical review. E.
[49] F. Chapin,et al. Planetary boundaries: Exploring the safe operating space for humanity , 2009 .