Preprocessing layers applied to an observation space that contains direction and position of the agent as well as a global observation

Web: https://www.reddit.com/r/reinforcementlearning/comments/ujtmgt/preprocessing_layers_applied_to_an_observation/

I'm trying to figure out what this code is doing but I have a hard time understanding the observation space here. The observation space is passed as an input, then there are some preprocessing layers (built as a dictionary) in which:

\- The key "policy state" (which is the hidden state of the LSTM that is controlling the policy) is associated to a few linear layers
\- The key "image" is associated to conv nets
\- The key "direction" (which …

global reinforcementlearning space