Customisable 3D benchmark for assessing generalisation in Reinforcement Learning.
MazeExplorer is a customisable 3D benchmark for assessing generalisation in Reinforcement Learning.
This repository contains the code for the MazeExplorer Gym Environment along with the scripts to generate baseline results.
By Luke Harries*, Sebastian Lee*, Jaroslaw Rzepecki, Katja Hofmann, and Sam Devlin.
* Joint first author
The goal is to navigate a procedurally generated maze and collect a set number of keys.
The environment is highly customisable, allowing you to create different training and test environments.
The following features of the environment can be configured:
- Number of maps
- Map Size (X, Y)
- Maze complexity
- Maze density
- Random/Fixed keys
- Random/Fixed textures
- Random/Fixed spawn
- Number of keys
- Environment Seed
- Episode timeout
- Reward clipping
- Frame stack
- Action frame repeat
- Actions space
- Specific textures (Wall, ceiling, floor)
from mazeexplorer import MazeExplorer train_env = MazeExplorer(number_maps=1, size=(15, 15), random_spawn=True, random_textures=False, keys=6) test_env = MazeExplorer(number_maps=1, size=(15, 15), random_spawn=True, random_textures=False, keys=6) # training for _ in range(1000): obs, rewards, dones, info = train_env.step(train_env.action_space.sample()) # testing for _ in range(1000): obs, rewards, dones, info = test_env.step(test_env.action_space.sample())
- Install the dependencies for VizDoom: Linux, MacOS or Windows.
pip3 install virtualenv pytest
- Create a virtualenv and activate it
- Git clone this repo
git clone https://github.com/microsoft/MazeExplorer
- cd into the repo:
- Pull the submodules with
git submodule update --init --recursive
- Install the dependencies:
pip3 install -e .
- Run the tests:
The information to reproduce the baseline experiments is shown in
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