Python Plays Grand Theft Auto 5 - Reboot
This is a reboot of the project from 2017, but with a whole new approach. This time, instead of collecting data by hand and training a classification model, we opted to create a whole new system for the data collecting, training and playing, and to do all of that live. Our models are now using regression (with other output types being still possible), training data is collected automatically and we stream models during their training phase so anyone can join and watch the progress.
Our AI, besides having multiple alter-egos (models), is still called Charles, like in 2017.
For a full explanation, refer to the System
page, but for a quick summary - weβre using a central server that all other parts connect to. The Data Collectors
run in separate GTA5 instances and our NPCs are collecting and Balancing
the data, which is sent through the Server
to the Trainer
. The Trainer
buffers these data, trains the model and updates the Player
. The Player
is using the Dual-Camera
system to play so we can watch the 3rd person camera (also called cinematic camera) while the model is fed the Hood Camera
.
For more information, refer to these other pages:
- this page - main page with the project progress
System
- describing how all parts are working together to train and inference the modelsCameras
- how we created multiple camerasConvcam
- what the Convcam is and how we use itData Balancing
- how we are balancing regression dataNPCs
- our custom NPCs playing in the GTA5 to collect data automaticallyPurpose
- a way to let the model βknowβ where to driveStorage and Buffer
- how are we managing the training data and why random batches are importantUnstuck
- how to make the car not be stuck anywhereXception
- the first CNN backbone architecture usedInceptionResNetv2
- the second, more successful, CNN backbone architecture usedTensorboard logs
- all of our Tensorboard logs for all of the trained models- The Original Project From 2017
model_0001_xception
- the first loggedXception
model,Balancing_v1
model_0002_xception
- tryingBalancing_v2
and a custom modelmodel_0003_xception
-Balancing_v3
and back to a standard modelmodel_0004_inceptionresnetv2
- trying a bigger model -InceptionResNetv2
model_0005_inceptionresnetv2
- transfer-learning attemptmodel_0006_inceptionresnetv2
- added stacked imagesmodel_0007_inceptionresnetv2
- trying a history inputsmodel_0008_irv2_data_td
- history with recurrent layersmodel_0009_irv2_cr_tl
- history with recurrent layers with transfer-learningmodel_0010_irv2_tcb
- dual-backboneInceptionResNetv2
model_0011_x_tcb
- dual-backboneXception
model_0012_regnet
- TBA
Stream and current stream layout:
The progress is being streamed on Twitch: https://www.twitch.tv/sentdex. Below we keep a list of dates and streamed models.
- top-left tile is the 3rd person camera of the car driving, the main camera to observe Charles
- this main tile, in its lower-right corner, shows the current model and when it's been created
- top-right tile called
Hood Camera
is exactly what the models "see" - the input to the convolutional backbone of the model - middle-right tile called
Convcam
shows the reshaped output of the CNN backbone and lets us observe how the CNN part trains - bottom-right tile called
Player Console
shows current driving predictions along with additional information - bottom-middle tile called
Server/Trainer Console
shows training progress along with some basic training information - bottom-left tile called
Tensorboard
shows the loss of the training process
Driving examples:
Model list:
- The Original Project From 2017 - how this started
model_0001_xception
- the first loggedXception
model,Balancing_v1
model_0002_xception
- tryingBalancing_v2
and a custom modelmodel_0003_xception
-Balancing_v3
and back to a standard modelmodel_0004_inceptionresnetv2
- trying a bigger model -InceptionResNetv2
model_0005_inceptionresnetv2
- transfer-learning attemptmodel_0006_inceptionresnetv2
- added stacked imagesmodel_0007_inceptionresnetv2
- trying a history inputsmodel_0008_irv2_data_td
- history with recurrent layersmodel_0009_irv2_cr_tl
- history with recurrent layers with transfer-learningmodel_0010_irv2_tcb
- dual-backboneInceptionResNetv2
model_0011_x_tcb
- dual-backboneXception
model_0012_regnet
- TBA
Streaming timeline
May 1st:
- Started streaming
model_0003_xception_v2
from scratch
May 2nd:
- Stopped streaming
model_0003_xception_v2
at batch 15250 - Started streaming
model_0003_xception_v1
from batch 24500 (pre-trained off-stream)
May 7th:
- Stopped streaming
model_0003_xception_v1
at batch 162650 - Started streaming
model_0004_inceptionresnetv2_v1
from scratch - Stopped streaming
model_0004_inceptionresnetv2_v1
at batch 1250 - Started streaming
model_0004_inceptionresnetv2_v2
from scratch - Stopped streaming
model_0004_inceptionresnetv2_v2
after ~160 batches - Started streaming
model_0003_xception_v1
from batch 162650 (continuing training) - Stopped streaming
model_0003_xception_v1
at batch 165250 - Started streaming
model_0004_inceptionresnetv2_v1
from batch 1250 (continuing training)
May 8th:
- Stopped streaming
model_0004_inceptionresnetv2_v1
at batch 12000 - Started streaming
model_0005_inceptionresnetv2_v1
from scratch - Stopped streaming
model_0005_inceptionresnetv2_v1
at batch 3500 - Started streaming
model_0004_inceptionresnetv2_v3
from batch 22250 (pre-trained off-stream)
May 14th:
- Stopped streaming
model_0004_inceptionresnetv2_v3
at batch 184250 - Started streaming
model_0006_inceptionresnetv2_v1
from batch 18750 (pre-trained off-stream)
May 16th:
- Stopped streaming
model_0006_inceptionresnetv2_v1
at batch 60500 - The stream has been offline for several days since we had to send back the Comino machine and setting up another one took longer than we expected
May 28th:
- After new machine was set and new models developed, we restarted streaming
- Started streaming
model_0009_irv2_cr_tl_v1
from scratch
May 29th:
- Stopped streaming
model_0009_irv2_cr_tl_v1
at batch 19250 - Started streaming
model_0004_inceptionresnetv2_v3
from batch 184250 (continuing training)
July 5th:
- Stopped streaming
model_0004_inceptionresnetv2_v3
at batch 752000 - The stream has been stopped as there have been no further improvements in the model and to catch up with documentation and do some off-stream development
Project timeline
This is not a full project log and contains only the key changes that are visible on the stream (and a few from before the stream has started). There are many more things going on "behind the scenes".
2017:
- Development of The Original Project From 2017
November 2021:
- The idea of bringing back this project has born
Early 2022:
- The project has started the development phase of the main system, the initial models, and the game mod
April 8th:
- Finished
Balancing_v1
- Started training
model_0001_xception_v1
from scratch - Stopped training
model_0001_xception_v1
at batch 10800 - Started training
model_0001_xception_v2
from scratch
April 9th:
- Stopped training
model_0001_xception_v2
at batch 7150 - Started training
model_0001_xception_v3
from scratch - Started training
model_0001_xception_v4
from scratch - Stopped training
model_0001_xception_v3
at batch 18850 - Started training
model_0001_xception_v5
from scratch
April 10th:
- Stopped training
model_0001_xception_v5
at batch 25200 - Stopped training
model_0001_xception_v3
at batch 23300 - Started training
model_0001_xception_v6
from scratch
April 11th:
- The first data histogram has been created to see the
Data Balancing
April 15th:
- Stopped training
model_0001_xception_v6
at batch 176850
April 19th:
- Started training
model_0002_xception_v1
from scratch
April 22nd:
- Started training
model_0003_xception_v1
from scratch
April 24th:
- Stopped training
model_0003_xception_v1
at batch 24500
April 30th:
- Stopped training
model_0002_xception_v1
at batch 249750
May 1st:
- The Convcam has been created
- Started streaming on Twitch.tv
- Started streaming
model_0003_xception_v2
from scratch
May 2nd:
- Stopped streaming
model_0003_xception_v2
at batch 15280 - Started streaming
model_0003_xception_v1
from batch 24500 (pre-trained off-stream) - Started training
model_0003_xception_v3
from scratch
May 3rd:
- Stopped training
model_0003_xception_v3
at batch 17500 - Started training
model_0003_xception_v4
from scratch - Stopped training
model_0003_xception_v4
at batch 5150
May 7th:
- Stopped streaming
model_0003_xception_v1
at batch 162650 - Started streaming
model_0004_inceptionresnetv2_v1
from scratch - Stopped streaming
model_0004_inceptionresnetv2_v1
at batch 1250 - Started streaming
model_0004_inceptionresnetv2_v2
from scratch - Stopped streaming
model_0004_inceptionresnetv2_v2
after ~160 batches - Started streaming
model_0003_xception_v1
from batch 162650 (continuing training) - Stopped streaming
model_0003_xception_v1
at batch 165350 - Started training
model_0004_inceptionresnetv2_v3
from scratch - Started streaming
model_0004_inceptionresnetv2_v1
from batch 1250 (continuing training)
May 8th:
- Stopped streaming
model_0004_inceptionresnetv2_v1
at batch 12000 - Started streaming
model_0005_inceptionresnetv2_v1
from scratch - Stopped streaming
model_0005_inceptionresnetv2_v1
at batch 3500 - Stopped training
model_0004_inceptionresnetv2_v3
at batch 22250 - Started streaming
model_0004_inceptionresnetv2_v3
from batch 22250 (pre-trained off-stream)
May 13th:
- Started training
model_0006_inceptionresnetv2_v1
from scratch
May 14th:
- Stopped training
model_0006_inceptionresnetv2_v1
at batch 18750 - Stopped streaming
model_0004_inceptionresnetv2_v3
at batch 184250 - Started streaming
model_0006_inceptionresnetv2_v1
from batch 18750 (pre-trained off-stream)
May 14th:
- Started training
model_0006_inceptionresnetv2_v2
from scratch - Stopped training
model_0006_inceptionresnetv2_v2
at batch 5750
May 16th:
- Stopped streaming
model_0006_inceptionresnetv2_v1
at batch 60500 - Stream has been offline for several days since we had to send back the Comino machine and setting up another one took longer than we expected
May 22nd:
- Started training
model_0007_inceptionresnetv2_v2
from scratch - Stopped training
model_0007_inceptionresnetv2_v2
at batch 10500
May 23th:
- Started training
model_0008_inceptionresnetv2_v1
from scratch
May 24th:
- Stopped training
model_0008_inceptionresnetv2_v1
at batch 35000 - Started training
model_0008_inceptionresnetv2_v2
from scratch - Started training
model_0008_inceptionresnetv2_v3
from scratch
May 25th:
- Stopped training
model_0008_inceptionresnetv2_v2
at batch 19000 - Stopped training
model_0008_inceptionresnetv2_v3
at batch 17000 - Started training
model_0008_inceptionresnetv2_v4
from scratch - Started training
model_0008_inceptionresnetv2_v5
from scratch
May 26th:
- Stopped training
model_0008_inceptionresnetv2_v5
at batch 37250
May 28th:
- Stopped training
model_0008_inceptionresnetv2_v4
at batch 35500 - After new machine was set and new models developed, we restarted streaming
- Started streaming
model_0009_irv2_cr_tl_v1
from scratch - Started training
model_0009_irv2_cr_tl_v2
from scratch
May 29th:
- Stopped streaming
model_0009_irv2_cr_tl_v1
at batch 19250 - Started streaming
model_0004_inceptionresnetv2_v3
from batch 184250 (continuing training)
May 30th:
- Stopped training
model_0009_irv2_cr_tl_v2
at batch 20000 - Started training
model_0009_irv2_cr_tl_v3
from scratch
May 31th:
- Stopped training
model_0009_irv2_cr_tl_v3
at batch 15000 - Started training
model_0009_irv2_cr_tl_v4
from scratch
June 1th:
- Stopped training
model_0009_irv2_cr_tl_v4
at batch 50000
June 3rd:
- Started training
model_0009_irv2_cr_tl_v5
from scratch - Created model line
model_0010_irv2_tcb
, none of the models were trained as of yet - Created model line
model_0011_x_tcb
, none of the models were trained as of yet
June 4th:
- Stopped training
model_0009_irv2_cr_tl_v5
at batch 52500
June 9th:
- Started training
model_0008_inceptionresnetv2_v6
from scratch
June 10th:
- Stopped training
model_0008_inceptionresnetv2_v6
at batch 45250 - Started training
model_0008_inceptionresnetv2_v7
from scratch
June 13th:
- Stopped training
model_0008_inceptionresnetv2_v7
at batch 75000
July 5th:
- Stopped streaming
model_0004_inceptionresnetv2_v3
at batch 752000 - The stream has been stopped as there have been no further improvements in the model and to catch up with documentation and do some off-stream development