ce5ffebe294eeeb088abfdc53d73b1c98c2565f2,src/garage/np/algos/batch_polopt.py,BatchPolopt,process_samples,#BatchPolopt#Any#Any#,80

Before Change

            tensor_utils.pad_tensor_dict(p, max_path_length) for p in env_infos
        ])

        terminals = [path["dones"] for path in paths]

        valids = [np.ones_like(path["returns"]) for path in paths]
        valids = tensor_utils.pad_tensor_n(valids, max_path_length)

        lengths = np.asarray([v.sum() for v in valids])

        ent = np.sum(self.policy.distribution.entropy(agent_infos) *
                     valids) / np.sum(valids)

        undiscounted_returns = self.evaluate_performance(
            itr,
            dict(env_spec=None,
                 observations=obs,
                 actions=actions,
                 rewards=rewards,
                 terminals=terminals,
                 env_infos=env_infos,
                 agent_infos=agent_infos,
                 lengths=lengths,
                 discount=self.discount))

        self.episode_reward_mean.extend(undiscounted_returns)

After Change

        undiscounted_returns = log_performance(
            itr,
            TrajectoryBatch.from_trajectory_list(self.env_spec, paths),
            discount=self.discount)

        self.episode_reward_mean.extend(undiscounted_returns)

Instances