We introduce gym-DSSAT, a gym environment for crop management tasks, that is easy to use for training Reinforcement Learning (RL) agents. gym-DSSAT is based on DSSAT, a state-of-the-art mechanistic crop growth simulator. We modify DSSAT so that an …
Addressing a real world sequential decision problem with Reinforcement Learning (RL) usually starts with the use of a simulated environment that mimics real conditions. We present a novel open source RL environment for realistic crop management …