New http://keras.io RL code example: applying Actor-Critic to the pole balancing problem, by @NandanApoorv
https://keras.io/examples/rl/actor_critic_cartpole/ …pic.twitter.com/h34zq9DzJ1
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At USAFA, in our astrophysics classes, we were presented with this very problem. Which we had to solve using an analog computer. Why is this an astrophysics problem, one may ask? Because this is the key problem to controlling the gimbals of a rocket engine for vector thrust.
Very cool! This is also what keeps quadcopter drones in the air (which is how I'm familiar with the problem). Reinforcement learning is probably the least efficient way to solve this. A PID controller would suffice.
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I'm reminded of Boston Dynamics' approach to robotics. While many people were trying to use RL to teach quadrupedal robots to walk, BD went for a ML-free approach based entirely on control theory. And got much better results, before anyone else.
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Precisely. The mathematics behind this particular problem are well understood...which means that a) there is a direct and simple solution to it via symbolic computation and b) this is a demonstration of the computational equivalence of symbolic and neural methods.
End of conversation
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In the end, a lot of what we know and love as modern computing was formed during the lamentations of World War II and the Cold War. In a very real sense, computing has been woven on the loom of sorrow. https://computingthehumanexperience.com/presentations/
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People would be surprised to know how far PID control took us. That’s partially the reason most of the industry is still hanging on with it 100 years on... Minorsky, Nicolas (1922). "Directional stability of automatically steered bodies"
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