Code for procedurally synthesizing videos used for constructing the trials in the dataset, AGENT, proposed in the paper: AGENT: A Benchmark for Core Psychological Reasoning.

• Clone the repository
• Download agent 3d models here and extract the folder inside the AGENT/ folder
• You can run the script in two modes:
  • To run trajectories

  python main.py --out-dir data --config-file configuration_files.json --config-start 0 --config-end 1 --scene-type scene_1
  

  • To run replay

  python main.py --out-dir data --config-file configuration_files.json --config-start 0 --config-end 100 --scene-type replay_scene
  

• We provide dataset in two bundles: Training & validation set, and Testing set
• The dataset is divided into 4 scenarios.
  • scenario_1_goal_preferences
  • scenario_2_action_efficiency
  • scenario_3_unobserved_constraints
  • scenario_4_cost_reward_trade_offs
• Each scenario is further divided into subtypes.
• Each subtype then contains will then contain train set and validation set or testing set depending on which zip folder you are inside.
• Each of the train, test and validation folders will contain the final trial sets. The number of trials is summarized in this

• For each trial we provide RGB and Depth map images as well as state data
• Each Trial folder is organized as :
  • Familiarization and expected or unexpected video or both depending on which set (train, validation, test) you are in and have similar structure.
  • Each folder contains:
    • images_c/ folder: Collection of numbered images/frames. Prefix used are: img_: RGB, id_: Segmentation mask, depth_: Depth map
    • Videos: scene_c.mp4 RGB image video, mask_scene_c.mp4 segmentation map video, depth_scene_c.mp4 depth map video
    • state_info.json : This JSON contains list of states. The len of list is equal to number of RBG image/ Depth images/Segmentation mask. Each element in list is dictionary with keys being object name and value being object attributes like position, velocity, rotation, etc. More information
    • scene_config.json: This contains dictionary of objects in the scene. This is used by the procedural generation script to recreate the scene.

• The state data which is list of dictionary, each of which represents state at each frame, uses different keys to represent objects.
• These keys include:
  • agent: Agent in the scene. Scenes will always have 1 agent
  • goal, goal_<side number>: Goal objects in scene. If there are more than one goal object than it will have number indicating which side it is on. 1 is left and 2 is right of agent
  • obstacle, obstacle_<side number>: This object is the plain wall barrier. Scene with multiple barrier will have side numbers
  • ramp_slope_<side number>, ramp_platform_<side number>, ramp_slope, ramp_platform: Each ramp is composed of the platform and slope. Each piece has its own set of attributes
  • barrier_<side number>_with_door_<piece number>, barrier_with_door_<piece number>: This is a plain wall with door cutout in the middle. This obstacle is composed of 3 pieces indicated by the piece number
  • pit_bridge_<side number>, pit_side_part_<side number>: Pit is formed by 2-3 pieces. In some scenario the pit sides will be connected by bridge
  • occluder: In Unobserved constraints' scenario the occluder is used to hide the obstacle.

If you use this code in your research, please consider citing.

@article{shu2021agent,
  title={AGENT: A Benchmark for Core Psychological Reasoning},
  author={Shu, Tianmin and Bhandwaldar, Abhishek and Gan, Chuang and Smith, Kevin A and Liu, Shari and Gutfreund, Dan and Spelke, Elizabeth and Tenenbaum, Joshua B and Ullman, Tomer D},
  journal={The Thirty-eighth International Conference on Machine Learning (ICML)},
  year={2021}
}