update to pymdp.Agent:

- in case inductive matrix `I` is not present,do not update it when updating transition parameters. This avoids an error in `tree_at` for updating None-valued leaves (`self.I` will be None in case `self.use_inductive = False`)

pymdp/agent.py

@@ -351,10 +351,9 @@ def infer_parameters(self, beliefs_A, outcomes, actions, beliefs_B=None, lr_pA=1
             # if you have updated your beliefs about transitions, you need to re-compute the I matrix used for inductive inferenece
             if self.use_inductive and self.H is not None:
                 I_updated = vmap(control.generate_I_matrix)(self.H, E_qB, self.inductive_threshold, self.inductive_depth)
+                agent = tree_at(lambda x: (x.B, x.pB, x.I), agent, (E_qB, qB, I_updated))
             else:
-                I_updated = self.I
-
-            agent = tree_at(lambda x: (x.B, x.pB, x.I), agent, (E_qB, qB, I_updated))
+                agent = tree_at(lambda x: (x.B, x.pB), agent, (E_qB, qB))
 
         return agent