Latest GLFW/WebGL Fork: https://github.com/mrbid/PoryDrive-2.0
The above link has the latest fun version of the game to play, the one in this repository is the first release which is a bit of a mess.
A 3D driving game with neural networks.
- Play Online: https://mrbid.github.io/porydrive
- Flathub: https://flathub.org/apps/com.voxdsp.PoryDrive
- Windows: https://notabug.org/attachments/d49923d5-230c-408a-9440-693ca8462bb6
- Source Code: https://notabug.org/Vandarin/PoryDrive
This game is capped to 100-144 FPS, if your machine cannot keep up it will just simulate the game a little slower. The latest linux fork has much better timing code. selu_main.c and keras_main.c use an adaptive framerate but the game simulation gets worse below 100 fps and unplayable under 30 fps.
I have always liked racing games, ever since the original V-Rally I was fascinated by the bouncy "realistic" physics of the vehicles, in 2006 I would make my first attempt at a 3D car game in C++ using the Ogre3D rendering engine in the game TeapotHunt; basic, and with no acceleration or deceleration because at that point I did not really understand floating-point numbers. By 2009 my Math had improved a lot thanks to the help of my university teachers, most specifically it was Tyrone Davison who taught me or well helped me realise the importance of floating-point math. In my final year of university, I used Bullet Physics and DirectX 9 with C++ to make another racing game called Burning Rubber with some friends for the Team Working module. It has been a decade since then and I am overjoyed that I have finally revisited this passion of mine having created a basic car physics simulation from scratch with self-guessed knowledge of car physics.
Drive around and "collect" Porygon, each time a Porygon is collected a new one will randomly spawn somewhere on the map. A Porygon colliding with a purple cube will cause it to light up blue, this can help find them. Upon right clicking the mouse the view will switch between Ariel and Close views, in the Ariel view it is easier to see which of the purple cubes that the Porygon is colliding with.
ESCAPE= Focus/Unfocus Mouse LookN= New GameW= Drive ForwardA= Turn LeftS= Drive BackwardD= Turn RightSpace= Breaks1-5= Car Physics config selection (5 loads from file)
F= FPS to consoleP= Player stats to consoleO= Toggle auto driveI= Toggle neural driveL= Toggle dataset logging
Mouse Button4= Zoom Snap Close/ArielMouse Click Right= Zoom Snap Close/ArielMiddle Scroll= Zoom in/out
It is possible to tweak the car physics by creating a config.txt file in the exec/working directory of the game, here is an example of such config file with the default car physics variables.
maxspeed 0.0095
acceleration 0.0025
inertia 0.00015
drag 0.00038
steeringspeed 1.2
steerinertia 233
minsteer 0.32
maxsteer 0.55
steeringtransfer 0.023
steeringtransferinertia 280
maxspeed- top travel speed of car.acceleration- increase of speed with respect to time.inertia- minimum speed before car will move from a stationary state.drag- loss in speed with respect to time.steeringspeed- how fast the wheels turn.steerinertia- how much of the max steering angle is lost as the car increases in speed (crude steering loss).minsteer- minimum steering angle as scalar (1 = 180 degree) attainable after steering loss caused bysteeringintertia.maxsteer- maximum steering angle as scalar (1 = 180 degree) attainable at minimal speeds.steeringtransfer- how much the wheel rotation angle translates into rotation of the body the wheels are connected to (the car).steeringtransferinertia- how much thesteeringtransferreduces as the car speed increases, this is related tosteerinertiato give the crude effect of traction loss of the front tires as speed increases and the inability to force the wheels into a wider angle at higher speeds.
Auto Drive is based on a simple concept similar to that of the Wall follower maze-solving algorithm the principle relies on the fact that if you always turn in one direction and aim to reduce the angle of direction between the agent and the target you will eventually hit the target. On top of this, there is a rule to switch the turn direction if the distance from the target increases by more than a threshold scaled by a scalar based on the distance from the target. It gives a pretty good effect and with competitive results to human players.
The Machine Learning or "neural" agents use a modified version of TFCNNv1 which optimises for a tanh output. The dataset was captured from ~6.4 hours of the Auto Drive algorithm playing the game with the ScarletFast car physics config (dataset not supplied due to file size, please train your own, read the trainer.c header). Two agents are trained from this dataset, the steering agent and gasing agent respectively. One controlling steering and the other controlling speed. The file globaldef.h holds the variables that configure the size (HIDDEN_SIZE) and complexity (HIDDEN_LAYERS) of the neural network. If these definitions are changed the neural network will need to be retrained by executing train.sh. Other hyperparameters can be configured in the trainer.c file itself after the createNetwork() function.
There is one disadvantage to these two systems; they break the laws of acceleration and turn speed. These algorithms can set arbitrary wheel turn angles and speeds with disregard to acceleration limits or wheel turn speed limits. A post-process could be added to enforce that transitions on these variables stay within such limits.
- car normal dir x
- car normal dir y
- (car_pos - porygon_pos) normal dir x
- (car_pos - porygon_pos) normal dir y
- angle between both normal dir's (Dot product)
- Euclidean distance between car and porygon
- car steering angle
- car speed
HIDDEN_LAYERS1HIDDEN_SIZE64OPTIM_ADAGRADDROPOUT0.3BATCHES32ACTIVATORtanhWEIGHT_INIT_UNIFORM_LECUN
Good results for a small 73kb network. Sometimes locates porygon quickly. Weights provided steernet and gasnet.
Notes: Increasing the hidden layers or dropout seems to have a negative effect on the trained results.
In hindsight I think the Steering Agent may have had better results without being trained using Euclidean distance as an input. Just the normal directions may have been enough information because it would probably have been easier to optimise for.
Try a SELU network TFCNNv2.1 to see if this has improvements with more hidden layer depth.(SELU support has been added, needs some tweaking still as I am yet to get good trained results, WIP, selu_trainer.c & selu_main.c, honestly I don't think it's worth the time investment when Tensorflow Keras will yeild better results with less time invested.)Try some networks in Tensorflow Keras, FNN with ADAM and a CNN.(the Keras version keras_main.c & train.py trains one network with two tanh outputs, one for steering and one for speed.)
Keras trains much better than my personal neural networks, partly because Keras supports the superior ADAM optimiser.
To train the network in Keras first you need to split the dataset.dat file into x targets (input data) and y targets (input labels for training). This is done by compiling and running the splitter.c program which will output dataset_x.dat and dataset_y.dat. Then you need to run train.py which will output porygon_model/keras_model. Then to boot the PoryDrive game you need to run pred.py which will execute a daemon that will take data from the PoryDrive game using a RAM file in /dev/shm/porydrive_input.dat, run it through the Keras model, and then return the result back to a second RAM file /dev/shm/porydrive_r.dat. Once pred.py is running you can compile and execute the Keras version of PoryDrive by executing compile_keras.sh.
The hyperparameters for the Keras network can be configured near the header of train.py.
It doesn't really seem to get any better than 1 hidden layer with 32 - 64 units.
It would be interesting to now train this Keras model on data collected from human input and not the simple "AutoDrive" algorithm.
I have forked this project to it's own organisation to keep track of it's development better, in the new repositories I upload full datasets and manage the directory structure in a slightly cleaner and concise manner. If this is something you are interested in, the latest updates to this project in respect to Machine Learning are developed over at this git; https://github.com/PoryDrive
If you are interested in just the game with no Auto-Drive or Neural Networks then please refer to Release6.
https://snapcraft.io/porydrive
https://github.com/mrbid/porydrive/raw/main/PoryDrive-x86_64.AppImage
https://github.com/mrbid/porydrive/raw/main/porydrive
TBA
https://github.com/mrbid/porydrive/raw/main/porydrive.exe
https://github.com/mrbid/porydrive/raw/main/glfw3.dll
https://downloadfree3d.com/3d-models/vehicles/car/bmw-e34/
https://www.cgtrader.com/free-3d-models/character/fantasy-character/porygon
https://www.cgtrader.com/free-3d-models/science/laboratory/dna-molecule-3d-model
https://pixels.com/featured/1-bmw-m5-e34-tuning-bmw-5-series-tunned-m5-hervey-dopson.html
https://gamerant.com/pokemon-sword-shield-porygon-porygon2-porygonz-isle-of-armor-dlc/ [src]
http://www.forrestwalter.com/icons/