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T1  - Reinforcement learning in optimizing forest management 
SN  -  /  
UR  - http://hdl.handle.net/10138/335816 
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A1  - Malo, Pekka; Tahvonen, Olli; Suominen, Antti; Back, Philipp; Viitasaari, Lauri 
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Y1  - 2021 
LA  - eng 
AB  - We solve a stochastic high-dimensional optimal harvesting problem by using reinforcement learning algorithms developed for agents who learn an optimal policy in a sequential decision process through repeated experience. This approach produces optimal solutions without discretization of state and control variables. Our stand-level model includes mixed species, tree size structure, optimal harvest timing, choice between rotation and continuous cover forestry, stochasticity in stand growth, and sto... 
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KW  - reinforcement learning; forestry; economics; stochasticity; continuous cover forestry; uneven-aged forestry; optimal rotation; mixed species; optimal harvesting; UNEVEN-AGED FORESTS; RISK; GROWTH; STAND; FIRE; OPTIMIZATION; ECONOMICS; ROTATION; 4112 Forestry 
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