text: # Feed the action into the environment and get new observation . 
  obs = step_TMaze !( Env , chosen_action ) 
  
  **end**(cid:6)  (cid:5) 
  
  The agent here started by moving to the cue location, and then proceeded to move to the 
  left arm repeatedly. The main objects of learning here were the reward condition state and 
  the**A**parameters for rewards under the two reward conditions. After observing the cue, 
  the agent updated its belief (correctly) to be certain of being in the left reward condition 
  (Figure 4). Over time, the agent learned the correct probability of receiving rewards in the 
  left arm (0.94 versus a correct 0.95). It did not learn the probabilities for the right arm; this 
  was because it never moved to that location, having already learnt that the left arm was 
  more likely to produce rewards (Figure 5). This would be less likely to be the case with 
  lower*γ*and*α*values, as well as a more entropic**C**. 