4427e66372ade50d4bc36a7cfe93bbdbf71234cc,ml/rl/test/gridworld/gridworld_continuous.py,GridworldContinuous,generate_samples,#GridworldContinuous#Any#Any#Any#,41

Before Change

        List[List[Dict[int, float]]],
        List[Dict[int, float]],
    ]:
        states, actions, propensities, rewards, next_states, next_actions, is_terminals, possible_next_actions, reward_timelines = self.generate_samples_discrete(
            num_transitions, epsilon, with_possible
        )
        continuous_actions = [self.action_to_features(a) for a in actions]
        continuous_next_actions = [
            self.action_to_features(a) if a is not "" else {} for a in next_actions
        ]
        continuous_possible_next_actions = []
        for possible_next_action in possible_next_actions:
            continuous_possible_next_actions.append(
                [
                    self.action_to_features(a) if a is not None else {}
                    for a in possible_next_action
                ]
            )

        return (
            states,
            continuous_actions,
            propensities,
            rewards,
            next_states,
            continuous_next_actions,
            is_terminals,
            continuous_possible_next_actions,
            reward_timelines,
        )

    def preprocess_samples(
        self,
        states: List[Dict[int, float]],

After Change

                ]
            )

        return Samples(
            states=samples.states,
            actions=continuous_actions,
            propensities=samples.propensities,
            rewards=samples.rewards,
            next_states=samples.next_states,
            next_actions=continuous_next_actions,
            is_terminal=samples.is_terminal,
            possible_next_actions=continuous_possible_next_actions,
            reward_timelines=samples.reward_timelines,
        )

    def preprocess_samples(
        self, samples: Samples, minibatch_size: int
    ) -> List[TrainingDataPage]:

Instances