3ce4fd552a89c6fd5e479ad3713418b036337ff6,mushroom_rl/algorithms/value/batch_td/fqi.py,FQI,fit,#FQI#,52

Before Change

        
        if self._boosted:
            if self._target is None:
                self._prediction = 0.
                self._next_q = 0.
                self._idx = 0
            fit = self._fit_boosted
        else:
            fit = self._fit

After Change

            if self._target is None:
                self._target = reward
            else:
                q = self.approximator.predict(next_state)
                if np.any(absorbing):
                    q *= 1 - absorbing.reshape(-1, 1)

                max_q = np.max(q, axis=1)
                self._target = reward + self.mdp_info.gamma * max_q

            self.approximator.fit(state, action, self._target, **self._fit_params)

Instances