Sokoban Environment 👾#
This is a Jax implementation of the Sokoban puzzle, a dynamic box-pushing environment where the agent's goal is to place all boxes on their targets. This version follows the rules from the DeepMind paper on Imagination Augmented Agents, with levels based on the Boxoban dataset from Guez et al., 2018[1]. The graphical assets were taken from gym-sokoban by Schrader, a diverse Sokoban library implementing many versions of the game in the OpenAI gym framework [2].
Observation#
grid: An Array (uint8) of shape(10, 10, 2). It represents the variable grid (containing movable objects: boxes and the agent) and the fixed grid (containing fixed objects: walls and targets).step_count: An Array (int32) of shape(), representing the current number of steps in the episode.
Object Encodings#
| Object | Encoding |
|---|---|
| Empty Space | 0 |
| Wall | 1 |
| Target | 2 |
| Agent | 3 |
| Box | 4 |
Actions#
The agent's action space is an Array (int32) with potential values of [0,1,2,3] (corresponding to [Up, Down, Left, Right]). If the agent attempts to move into a wall, off the grid, or push a box into a wall or off the grid, the grid state remains unchanged; however, the step count is incremented by one. Chained box pushes are not allowed and will result in no action.
Reward#
The reward function comprises:
- -0.1 for each step taken in the environment.
- +1 for each box moved onto a target location and -1 for each box moved off a target location.
- +10 upon successful placement of all four boxes on their targets.
Episode Termination#
The episode concludes when: - The step limit of 120 is reached. - All 4 boxes are placed on targets (i.e., the problem is solved).
Dataset#
The Boxoban dataset offers a collection of puzzle levels. Each level features four boxes and four targets. The dataset has three levels of difficulty: 'unfiltered', 'medium', and 'hard'.
| Dataset Split | Number of Levels |
|---|---|
| Unfiltered (Training) | 900,000 |
| Unfiltered (Validation) | 100,000 |
| Unfiltered (Test) | 1000 |
| Medium (Training) | 450,000 |
| Medium (Validation) | 50,000 |
| Hard | 3332 |
The dataset generation procedure and more details can be found in Guez et al., 2018 [1].
Graphics#
| Type | Graphic |
|---|---|
| Wall |  |
| Floor |  |
| Target |  |
| Box on Target |  |
| Box Off Target |  |
| Agent Off Target |  |
| Agent On Target |  |
Registered Versions 📖#
Sokoban-v0: Sokoban game with levels generated using DeepMind Boxoban dataset (unfiltered train).
References#
[1] Guez, A., Mirza, M., Gregor, K., Kabra, R., Racaniere, S., Weber, T., Raposo, D., Santoro, A., Orseau, L., Eccles, T., Wayne, G., Silver, D., Lillicrap, T., Valdes, V. (2018). An investigation of Model-free planning: boxoban levels. Available at https://github.com/deepmind/boxoban-levels
[2] Schrader, M. (2018). Gym-sokoban. Available at https://github.com/mpSchrader/gym-sokoban