Wilderness Scavenger: 3D Open-World FPS Game AI Challenge
This is a platform for intelligent agent learning based on a 3D open-world FPS game developed by Inspir.AI.
- 2022-05-16: improved engine backend (Linux) with better stability (v1.0)
- Check out Supported Platforms for download links.
- Make sure to update to the latest version of the engine if you would like to use depth map or enemy state features.
- 2022-05-18: updated engine backend for Windows and MacOS (v1.0)
With a focus on learning intelligent agents in open-world games, this year we are hosting a new contest called Wilderness Scavenger. In this new game, which features a Battle Royale-style 3D open-world gameplay experience and a random PCG-based world generation, participants must learn agents that can perform subtasks common to FPS games, such as navigation, scouting, and skirmishing. To win the competition, agents must have strong perception of complex 3D environments and then learn to exploit various environmental structures (such as terrain, buildings, and plants) by developing flexible strategies to gain advantages over other competitors. Despite the difficulty of this goal, we hope that this new competition can serve as a cornerstone of research in AI-based gaming for open-world games.
- A light-weight 3D open-world FPS game developed with Unity3D game engine
- Rendering-off game acceleration for fast training and evaluation
- Large open world environment providing high freedom of agent behaviors
- Highly customizable game configuration with random supply distribution and dynamic refresh
- PCG-based map generation with randomly spawned buildings, plants and obstacles (100 training maps)
- Interactive replay tool for game record visualization
We developed this repository to provide a training and evaluation platform for the researchers interested in open-world FPS game AI. For getting started quickly, a typical workspace structure when using this repository can be summarized as follows:
. ├── examples # providing starter code examples and training baselines │ ├── envs/... │ ├── basic.py │ ├── basic_track1_navigation.py │ ├── basic_track2_supply_gather.py │ ├── basic_track3_supply_battle.py │ ├── baseline_track1_navigation.py │ ├── baseline_track2_supply_gather.py │ └── baseline_track3_supply_battle.py ├── inspirai_fps # the game play API source code │ ├── lib/... │ ├── __init__.py │ ├── gamecore.py │ ├── raycast_manager.py │ ├── simple_command_pb2.py │ ├── simple_command_pb2_grpc.py │ └── utils.py └── fps_linux # the engine backend (Linux) ├── UnityPlayer.so ├── fps.x86_64 ├── fps_Data/... └── logs/...
fps_linux(requires to be manually downloaded and unzipped to your working directory): the (Linux) engine backend extracted from our game development project, containing all the game related assets, binaries and source codes.
inspirai_fps: the python gameplay API for agent training and testing, providing the core
Gameclass and other useful tool classes and functions.
examples: we provide basic starter codes for each game mode targeting each track of the challenge, and we also give out our implementation of some baseline solutions based on
ray.rllibreinforcement learning framework.
We support the multiple platforms with different engine backends, including:
- Linux: download the engine from Google Drive or Feishu (last updated on 2022-05-16)
- Windows: download the engine from Google Drive or Feishu (last updated on 2022-05-18)
- MacOS: download the engine from Google Drive or Feishu (last updated on 2022-05-18)
Installation (from source)
To use the game play API, you need to first install the package
inspirai_fps by following the commands below:
git clone https://github.com/inspirai/wilderness-scavenger cd wilderness-scavenger pip install .
We recommend installing this package with python 3.8 (which is our development environment), so you may first create a virtual env using
conda and finish installation:
$ conda create -n WildScav python=3.8 $ conda activate WildScav (WildScav) $ pip install .
Installation (from PyPI)
Note: this may not be maintained in time. We strongly recommend using the installation method above
Alternatively, you can install the package from PyPI directly. But note that this will only install the gameplay API
inspirai_fps, not the backend engine. So you still need to manually download the correct engine backend from the Supported Platfroms section.
pip install inspirai-fps
Loading Engine Backend
To successfully run the game, you need to make sure the game engine backend for your platform is downloaded and set the
engine_dir parameter of the
Game init function correctly. For example, here is a code snippet in the script
from inspirai_fps import Game, ActionVariable ... parser.add_argument("--engine-dir", type=str, default="../fps_linux") ... game = Game(..., engine_dir=args.engine_dir, ...)
Loading Map Data
To get access to some features like realtime depth map computation or randomized player spawning, you need to load the map data and load them into the
Game. After this, once you turn on the depth map rendering, the game server will automatically compute a depth map viewing from the player's first person perspective at each time step.
- Download map data from Google Drive or Feishu and decompress the downloaded file to your preferred directory (e.g.,
map_dirparameter of the
- Set the
map_idas you like
- Turn on the function of depth map computation
- Turn on random start location to spawn agents at random places
Read the following code snippet in the script
examples/basic.py as an example:
from inspirai_fps import Game, ActionVariable ... parser.add_argument("--map-id", type=int, default=1) parser.add_argument("--use-depth-map", action="store_true") parser.add_argument("--random-start-location", action="store_true") parser.add_argument("--map-dir", type=str, default="../map_data") ... game = Game(map_dir=args.map_dir, ...) game.set_map_id(args.map_id) # this will load the valid locations of the specified map ... if args.use_depth_map: game.turn_on_depth_map() game.set_depth_map_size(380, 220, 200) # width (pixels), height (pixels), depth_limit (meters) ... if args.random_start_location: for agent_id in range(args.num_agents): game.random_start_location(agent_id, indoor=False) # this will randomly spawn the player at a valid outdoor location, or indoor location if indoor is True ... game.new_episode() # start a new episode, this will load the mesh of the specified map
We have also developed a replay visualization tool based on the Unity3D game engine. It is similar to the spectator mode common in multiplayer FPS games, which allows users to interactively follow the gameplay. Users can view an agent's action from different perspectives and also switch between multiple agents or different viewing modes (e.g., first person, third person, free) to see the entire game in a more immersive way. Participants can download the tool for their specific platforms here:
- Windows: download the replay tool from Google Drive or Feishu
- MacOS: download the replay tool from Google Drive or Feishu
To use this tool, follow the instruction below:
- Decompress the downloaded file to anywhere you prefer.
- Turn on recording function with
game.turn_on_record(). One record file will be saved at the end of each episode.
Find the replay files under the engine directory according to your platform:
Copy replay files you want to the replay tool directory according to your platform and start the replay tool.
For Windows users:
- Copy the replay file (e.g.
FPSGameUnity.exeto start the application.
For MacOS users:
- Copy the replay file (e.g.
fps.appto start the application.
In the replay tool, you can:
- Select the record you want to watch from the drop-down menu and click PLAY to start playing the record.
- During the replay, users can make the following operations
- Press Tab: pause or resume
- Press E: switch observation mode (between first person, third person, free)
- Press Q: switch between multiple agents
- Press ECS: stop replay and return to the main menu