importd4rl# Import required to register environmentsenv=gym.make('maze2d-umaze-v1')dataset=env.get_dataset() (3) D4RL定义了标准的衡量指标 $$ normalized score =100\%* \frac{score- random_score}{export_score-random_score} $$ (4) D4RL提供了丰富的baseline基准,包括了常见的Offline算法,...
You can use theenv.get_normalized_score(returns)function to compute a normalized score for an episode, wherereturnsis the undiscounted total sum of rewards accumulated during an episode. The individual min and max reference scores are stored ind4rl/infos.pyfor reference. ...