https://github.com/juliareinforcementlearning/raycastworlds.jl
First-person 3D reinforcement learning environments in Julia
https://github.com/juliareinforcementlearning/raycastworlds.jl
julia raycasting reinforcement-learning reinforcement-learning-environments reinforcement-learning-playground
Last synced: 6 months ago
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First-person 3D reinforcement learning environments in Julia
- Host: GitHub
- URL: https://github.com/juliareinforcementlearning/raycastworlds.jl
- Owner: JuliaReinforcementLearning
- License: mit
- Created: 2021-04-11T13:54:30.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2022-01-25T14:31:51.000Z (over 3 years ago)
- Last Synced: 2024-04-13T21:20:22.900Z (about 1 year ago)
- Topics: julia, raycasting, reinforcement-learning, reinforcement-learning-environments, reinforcement-learning-playground
- Language: Julia
- Homepage:
- Size: 87.9 KB
- Stars: 8
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# RayCastWorlds
This package provides simple first-person 3D games that can also be used as reinforcement learning environments. It is inspired by [DeepMind Lab](https://github.com/deepmind/lab).
## Table of Contents
* [Getting Started](#getting-started)
* [Notes](#notes)
[List of Environments](#list-of-environments)
1. [SingleRoom](#singleroom)
## Getting Started
```julia
import RayCastWorlds as RCW
env = RCW.SingleRoomModule.SingleRoom()
# reset the game. All environments are randomized
RCW.reset!(env)
# get names of actions that can be performed in this environment
RCW.get_action_names(env)
# perform actions in the environment
RCW.act!(env, 1) # move forward
RCW.act!(env, 2) # move backward
RCW.act!(env, 3) # turn left
RCW.act!(env, 4) # turn right
# interactively play the game
# keybindings:
# `q`: quit
# `r`: reset
# `w`: move forward
# `s`: move backward
# `a`: turn left
# `d`: turn right
# `v`: toggle top view and camera view
RCW.play!(env)
# use the RLBase API
import ReinforcementLearningBase as RLBase
# wrap a game instance from this package to create an RLBase compatible environment
rlbase_env = RCW.RLBaseEnv(env)
# perform RLBase operations on the wrapped environment
RLBase.reset!(rlbase_env)
state = RLBase.state(rlbase_env)
action_space = RLBase.action_space(rlbase_env)
reward = RLBase.reward(rlbase_env)
done = RLBase.is_terminated(rlbase_env)
rlbase_env(1) # move forward
rlbase_env(2) # move backward
rlbase_env(3) # turn left
rlbase_env(4) # turn right
```
## Notes
### RayCaster
The core raycasting algorithm is implemented in the [`RayCaster`](https://github.com/Sid-Bhatia-0/RayCaster.jl) package.
### Units
There are 4 types of units used throughout the code:
1. `wu`: Stands for world units. These are usually floating point numbers representing positions in the real world.
1. `tu`: Stands for tile units. These are integers representing positions on the tile map.
1. `pu`: Stands for pixel units. These are integers representing positions on the visualization image.
1. `au`: Stands for angle units. These are integers representing angles from 0 to `num_directions`.
The height of the tile map correponds to the x-axis of the coordinate system (often indexed with `i`) and width correponds to the y-axis (often indexed with `j`). This keeps the coordinate system right-handed.
## List of Environments
1. ### SingleRoom
The objective of the agent is to navigate its way to the goal. When the agent tries to move into the goal tile, it receives a reward of 1 and the environment terminates.
Camera view:
Top view:
