def acquire_MLP(self):
"""
Our MLP uses a tanh to decide on the control input of the system.
"""
self._mlp = MLP(layers=[self.state_size] + self.internal_layers +
[self.policy_size],
dropout=self.dropout)
mlp_params, mlp_x, mlp_prediction, mlp_prediction_dropout = \
self._mlp.get()
for param_set in mlp_params:
self.params.extend(param_set)
self._prediction = self.boundsify(mlp_prediction)
self._prediction_dropout = self.boundsify(mlp_prediction_dropout)
self._x = mlp_x
