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Code for training a differential-drive agent how to navigate to a goal using gradient-free reinforcement learning via the Cross-Entropy Method. Multi-threaded CPU and CUDA GPU implementations are presented.
zachavis/RL-Agent-CEM
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To run all programs: Must be on Windows 10 64 bit. Must have Visual Studio 2019 it may be the case that you can directly run the built binaries in x64/release to see training i.e. the reward increase and tests run. For CEM CUDA: Must have CUDA 10.1 with support for visual studio 2019 installed, and added to system path Although it isn't used, must download Eigen library: http://eigen.tuxfamily.org/index.php?title=Main_Page In project properties, in config properties, in VCC++ directories, set the path for your eigen download. The default is C:\Toolkits\eigen-3.3.7\ build and run, however, I didn't have luck running from visual studio, and had to navigate to the build directory and run from the command line. For CEM Vehicle Update: Must download Eigen library: http://eigen.tuxfamily.org/index.php?title=Main_Page In project properties, in config properties, in VCC++ directories, set the path for your eigen download. The default is C:\Toolkits\eigen-3.3.7\ build and run For MotionPlanning.ipynb simulator/visualizer must pip install numpy and matplotlib must have file called "dd_parameters.m" in the same directory if CPU params, set USE_CPU to True, else False must load via python notebook software such as jupyter run all cells but the final, unless you want a video saved.
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Code for training a differential-drive agent how to navigate to a goal using gradient-free reinforcement learning via the Cross-Entropy Method. Multi-threaded CPU and CUDA GPU implementations are presented.
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