The sample efficiency of the RL algorithm, even for simple games, is not very good. This usually means that we will need a lot of episodes for the policy to learn to excel. Being able to run policy in an environment that can parallel and accelerate could be very helpful for the improvement - for example running a batch of browsers or tabs simultaneously :)
People born in different eras often develop different worldviews by the time they reach their 50s. Not everyone is lucky enough to be born in the "right time".
I am curious about the setup of 14 GPUs - what kind of platform (motherboard) do you use to support so many PCIe lanes? And do you even have a chassis? Is it rack-mounted? Thanks!
1. Start by learning a simulation tool, e.g. Mujoco (open source) or Isaac Sim.
2. Learn basics of optimal control and reinforcement learning, reproduce papers/ideas in the simulation.
3. Get your hands dirty on a cheap robot, and try deploy your trained model on it. For mobility and manipulation. Unitree Go1/Go2 for mobility, and robotic arms for manipulation.