Handy attributes#

gym-dssat provides observations to the agent as any RL environment ; but moreover the user can access additional hidden DSSAT’s state features for the sake of the analysis.

env._state and env.observation#


State and observation variables are defined as indicated in the Decision problems section.


Contains all the raw state variables which is retrieved at every time step from DSSAT-PDI. For example, these variables can be used to inspect the simulations or plot dynamics.


Observations are subsets of env._state. This corresponds to the agent perspective for the considered problem.


Histories are dictionaries that are filled during each episode and which are emptied with env.reset(). They are usefull for agent’s performance analysis. Each key contains nested lists of the form: [[values_episode_1], ..., [values_episode_n]].


self.history = {'observation': [...], 'action': [...], 'reward': [...]}


self._history = {'state': [...], 'action': [...], 'reward': [...]}