diff --git a/pymdp/agent.py b/pymdp/agent.py
index b28d26e9..de8363c8 100644
--- a/pymdp/agent.py
+++ b/pymdp/agent.py
@@ -604,74 +604,6 @@ def _infer_states_test(self, observation, distr_obs=False):
             return qs, xn, vn
         else:
             return qs
-
-    def infer_policies_old(self):
-        """
-        Perform policy inference by optimizing a posterior (categorical) distribution over policies.
-        This distribution is computed as the softmax of ``G * gamma + lnE`` where ``G`` is the negative expected
-        free energy of policies, ``gamma`` is a policy precision and ``lnE`` is the (log) prior probability of policies.
-        This function returns the posterior over policies as well as the negative expected free energy of each policy.
-
-        Returns
-        ----------
-        q_pi: 1D ``numpy.ndarray``
-            Posterior beliefs over policies, i.e. a vector containing one posterior probability per policy.
-        G: 1D ``numpy.ndarray``
-            Negative expected free energies of each policy, i.e. a vector containing one negative expected free energy per policy.
-        """
-
-        if self.inference_algo == "VANILLA":
-            q_pi, G = control.update_posterior_policies(
-                self.qs,
-                self.A,
-                self.B,
-                self.C,
-                self.policies,
-                self.use_utility,
-                self.use_states_info_gain,
-                self.use_param_info_gain,
-                self.pA,
-                self.pB,
-                E=self.E,
-                I=self.I,
-                gamma=self.gamma
-            )
-        elif self.inference_algo == "MMP":
-            if self.factorized:
-                raise NotImplementedError("Factorized inference not implemented for MMP")
-            
-            if self.sophisticated:
-                raise NotImplementedError("Sophisticated inference not implemented for MMP")
-
-
-            future_qs_seq = self.get_future_qs()
-
-            q_pi, G = control.update_posterior_policies_full(
-                future_qs_seq,
-                self.A,
-                self.B,
-                self.C,
-                self.policies,
-                self.use_utility,
-                self.use_states_info_gain,
-                self.use_param_info_gain,
-                self.latest_belief,
-                self.pA,
-                self.pB,
-                F = self.F,
-                E = self.E,
-                I=self.I,
-                gamma = self.gamma
-            )
-
-        if hasattr(self, "q_pi_hist"):
-            self.q_pi_hist.append(q_pi)
-            if len(self.q_pi_hist) > self.inference_horizon:
-                self.q_pi_hist = self.q_pi_hist[-(self.inference_horizon-1):]
-
-        self.q_pi = q_pi
-        self.G = G
-        return q_pi, G
     
     def infer_policies(self):
         """