![]() Actions are a single-dimensional NumPy array of 0 (left), 1 (straight) and 2 (right), while observations are a four-dimensional array where the first dimension are samples, and the other are (48, 80, 3) - RGB image size. Save format is gzipped pickle file containing a dictionary of 'actions' and 'observations'. You can also create your own dataset by recording frames and actions at 10 FPS. If you believe you have a model that has interesting results, feel free to reach out and we may try to train it on the full dataset. This is important for further seamless experience with player "playing" the environment - it needs to output coherent and believable images.ĭata collecting demo with visible road nodes (not included in the final training data):Īs mentioned above, we can't share our data collecting scripts, but we are providing sample dataset. Python script analyzes current car position and nearest road nodes to drive using different paths to cover all possible actions and car positions as best as possible. Game mod accepts steering commands from the Python script as well as limits the speed and sets other options like weather, traffic, etc. We are pulling road nodes from the game and apply math transformations to create paths, then we are spawning multiple cars at the same time and alternate them to pull multiple streams at the same time (to speedup training). ![]() It contains a simple driving AI (which we named DumbAI ) ). We created our own GTA5 mod accompanied by a Python script to collect the data. This is an environment created using Grand Theft Auto V. (you need a CUDA capable Nvidia GPU to run this demo)ĭownload and unzip or clone this repository:Ĭurrently, GTA V, Vroom and Cartpole are the only implemented data sources. GANTheftAuto output on the left, upscaled 4x for better visibility, and upsampled output (by a separate model) Playable demo The work is still in progress as we know that our results can be greatly improved still.
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