Supplementary Material for Quantifying Generalisation in Imitation Learning

Each split is entirely unique, and no labyrinth appears more than once in4 the entire dataset, regardless of its split. Each entry consists of five pieces of information:5 obs: the path to the image representation for that state;6 actions: the integer action performed for that solution in that state;7 rewards: the float reward received for that action in that state;8 episode_starts: the boolean status that states if it is the first state in an episode; and9 info: the textual information required to load the same labyrinth structure if needed.10 We provide images in each dataset since we believe that visual information is more useful to the11 imitation learning agent, and if a vector representation is needed, the info parameter allows researchers12 to load the same structure and the actions enables the recreation of the dataset in its vector format.13 Each observation is an image of size 600 600 3. Although each baseline trained in this work14 uses a 64 64 3input, we thought that providing a bigger image would benefit models requiring15 downsizing (e.g., the walls will not disappear during resizing).