A personal, experimental dive learning how to build C++ based machine learning systems.

• I ultimately want to explore the use of deep reinforcement learning to democratise the manufacturing domain
• I'm fascinated by the work of Deepmind / David Silver (loved his UCL lecture series)
• I'm enjoying reading Reinforcement Learning: An Introduction: Sutton, Barto (2018)
• I'm enjoying reading Deep Reinforcement Learning Hands-On: Maxim Lapan (2018)
• I think that https://gym.openai.com/ is cool
• I like bullet as a simulation environement, maybe even try godot?
• I learn by doing e.g. re-writing things from python -> C++
• PyTorch now has a wonderful C++ API
• I want my GPU to do something interesting
• I want to build the whole thing as a container native / ready to scale

• https://github.com/Omegastick/pytorch-cpp-rl (such as linking to torch C++ <-> openai gym with ZMQ)
• https://github.com/Shmuma/ptan / https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On
• https://github.com/lupoglaz/GodotGymAI

• Build Godot gym module https://github.com/thomas-gale/godot-gym-cpp
• Attempt to roughly follow exercises in Deep Reinforcement Learning Hands-On: Maxim Lapan (2018) but using godot game approximations and rewritten in C++
• Possibily give learn library similar structure to ptan?
• Finally, do something cool (maybe with b2a - build in a learning agent into the service providers of the virtual factory marketplace with the reward function set to maximise profit...)

• git clone (this repo)
• git submodule update --init --recursive
• Open directory in VSCode
• Follow the 'Reopen in container prompt'
• Enable things that pop up - like allowing C++ intellisense to use CMake

• VSCode command pallette (ctrl+shft+p)
• CMake: Configure
• Choose kit GCC for x86_64-linux-gnu 7.5.0
• Cmake: Build
• The target selection, debug and run commands are available at the bottom of the screen in VSCode