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Abstract Distinguishing forest types—primary, naturally regenerating, planted, and plantation forests—from agricultural tree crops and other land uses is essential for carbon accounting, biodiversity assessment, conservation planning, and supply-chain regulation. However, no existing global dataset resolves this typology at high spatial resolution.
Introduction Reinforcement Learning (RL) relies on Markov Decision Processes (MDP) as its cornerstone, a general framework under which vastly different problems can be casted. There is a clear separation in the class of MDPs between the finite discrete action setup, where an agent faces a finite number of possible actions, and the continuous action setup, where an agent faces an infinite number of actions.
(Submitted on 4 Oct 2018 ( v1 ), last revised 22 Feb 2019 (this version, v3)) Submission history From: Nikolay Savinov [ view email ] [v1] Thu, 4 Oct 2018 15:24:06 UTC (4,546 KB) [v2] Sat, 1 Dec 2018 17:39:39 UTC (3,901 KB) [v3] Fri, 22 Feb 2019 17:02:58 UTC (3,909 KB)
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