Gridworld environment made for navigation purposes. Check in the Description section for a detailed overview.

To install, do

git clone https://github.com/bmazoure/pixel_world.git
cd pixel_world
pip install -e .

The repo is structured as follows:

pixel_world/
 |-env_utils.py
 maps/
  |- room1.txt
  |- room2.txt
  |- ...

The environment can build arbitrary defined maps. For example, room5_medium.txt is defined as

###########
#        0#
####   ####
#         #
#S        #
###########

The environment has four actions: {↑,→,↓,←} corresponding to integers from 0 to 3 and encoded using standard basis vectors, which are then added to the agent's position and clipped to the environment's dimensions.

Four action typed are allowed:

• 1d_discrete: up, down, left, right;
• 1d_horizontal: left, right;
• 1d_vertical: up, down;
• 2d_continuous: 2d real valued vector taking values from [-1,1].

The state space of the environment is the Euclidean coordinate tuple (x,y). Alternatively, the observation tensor n x m x 3, where n,m are the dimensions of the gridworld, can be returned with the flag state_type=image.

Custom state types are allowed. Below are some default state types:

• #: Wall tile;
• S: Initial state;
• 0: Goal state, gives +R reward and ends the episode. Here, R~|N(0,1)|;
• ' ': Empty tile.

Each state is a DiscreteState instance with the following attributes:

• coords: {(x,y)|(x,y)∈ state space}, the Euclidean coordinates of the state;

• color: {[0,0,0],...,[255,255,255]}, the RGB pixel values of the state;

• terminal: {True/False}, whether the state is terminal, in which case the environment returns done=True;

• initial: {True,False}, whether the state is initial, i.e. these are the initial agent's coordinates;

• accessible: {True/False}, whether the agent can access this state. If the state is unaccessible (e.g. a wall), then the agent stays in the previous state;

• stochastic: {True/False}, whether the reward given upon transitionning into this state is sampled from a distribution, or is sampled at the beginning and remains fixed;

• reward_pdf: A callable function which samples rewards from the specified distribution. Below are examples of reward densities:

  • lambda: np.abs(np.random.normal(loc=1,scale=0.1,size=1)).item() - |N(0,1)|;
  • lambda: 10 - Dirac at 10;
  • lambda: np.random.uniform(0,1,size=1)).item() - |U(0,1)|.

• collectible: {True,False}, whether the rewards of the state are reset to the default value after the first visit during an episode.

This happens it three steps:

• Add the map text file following the constraints above into maps/;
• (Optional) Add a new navigation alphabet to pixel_world/envs/env_utils.py with custom rewards, symbols etc;
• In pixel_world/envs/__init__.py, register the new map with a unique name and a nagivation alphabet.

For any given map, we might be interested to move the goal state around to create a shared-dynamics setting.

The sampler=PixelWorld-Sampler class takes in a template environment, from which it erases all terminal states. After calling sampler.generate(train,train_split,test_split), it generates all valid configurations of goal states, sorts the maps by Euclidean distance between the goal and the agent and assigns the first train_split% of the tasks to the training set and the last test_split% to the test set.