text: The first observation modality had an observation for each of the positions in the maze, 
  and generally reflected the agent’s actual position perfectly. The second modality was the 
  agent’s observation of a reward or loss, which depended on the reward condition and the 
  position. The “no reward” observation was received whenever the agent was not occupying 
  either of the arm locations, where it did not observe any reward or loss. The third modality 
  was the observation of the cue, which depended on the reward modality so that the agent 
  could use it to infer the correct reward condition. The action was one-dimensional with four 
  options and was used to move between the different locations: 
  
  **Actions:**
  
  1. Move to centre location; 
  2. Move to right arm; 
  3. Move to left arm; 
  4. Move to cue location. 
  
  We now provide an example of using the package with a T-maze environment. First, 
  
  we installed and loaded the package: 
  (cid:7)  (cid:4) 
  
  **using**Pkg 
  Pkg . add ( ActiveInference ) 
  
  **using**ActiveInference 
  (cid:6)  (cid:5) 
  
  We first set up the environment or generative process: the T-maze. The T-maze is included 
  as a pre-made environment in ActiveInference, so we simply loaded it. We set the reward 
  probability for the reward condition to 95 percent: 