The training framework is based on a single-server / multiple-clients architecture. Once tmrl is installed, it is easy to use this environment in your own training framework. Real-Time Gym environment: tmrl comes with a real-time Gym environment based on rtgym. Python library: tmrl is a complete framework designed to help you successfully implement deep RL in your real-time applications (e.g., robots).Ī complete tutorial toward doing this is provided here. (note: only the MLP is supported at the moment, the rest is WIP).ĭeveloper features (real-time applications): To process raw camera images (snapshots), it uses a Convolutional Neural Network (CNN)
To process LIDAR measurements, tmrl uses a Multi-Layer Perceptron (MLP) or a Recurrent Neural Network (RNN). (note: only the LIDAR is supported at the moment, the rest is WIP). The car can use either a LIDAR (Light Detection and Ranging) computed from snapshots or the raw unprocessed snapshots in order to perceive its environment In parallel, this dataset is used to train an artificial neural network (policy) that maps observations (images, speed.) to relevant actions (gas, steering angle.).Īnalog control: tmrl controls the game using a virtual gamepad, which enables analog input. These algorithms store collected samples in a large dataset, called a replay memory. Training algorithms: tmrl lets you easily train policies in TrackMania with state-of-the-art Deep Reinforcement Learning algorithms such as Soft Actor-Critic (SAC) and Randomized Ensembled Double Q-Learning (REDQ). This is done through Deep Reinforcement Learning (RL). Tmrl is a python framework designed to help you train Artificial Intelligences (AIs), also called "policies", for your own robots or real-time video games. Tmrl provides a Gym environment for TrackMania that is easy to use. ? ML developers who are TM enthusiasts with no interest in learning this huge thing:
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Full tutorial here and documentation here. Tmrl is a python library designed to facilitate the implementation of deep RL applications in real-time settings such as robots and video games. Tutorial for you guys here and video of a pre-trained AI here (with a beginner introduction to the SAC algorithm). Tmrl enables you to train AIs in TrackMania with minimal effort. It is demonstrated on the TrackMania 2020 video game.
Tmrl is a distributed framework for training Deep Reinforcement Learning AIs in real-time applications.