It was quite enlightening for me to read Deep Reinforcement Learning Doesn’t Work Yet. And to be honest, a little depressing as well. I was vaguely aware of the challenges involved but only in a general sense. Just small tidbits here and there over the past few years, as I looked at this field with interest. Now that I finally got around to looking at reinforcement learning in more detail, I realized that it was overly optimistic of me to expect all major problems to have been solved by now.
My original motivation for getting into reinforcement learning was to make my Sawppy an autonomous rover. Based on what I’ve learned so far, my original hopes for Sawppy intelligence via reinforcement learning is extremely ambitious and still quite far away. If I want to do some deep RL projects more likely to succeed in the near term, I probably shouldn’t put it on a real physical rover. In all likelihood, whatever can be accomplished on a real robot using deep reinforcement learning could be done faster and more easily with some other AI technique.
It would certainly be nice if some aspect of Sawppy intelligence will eventually result in a research project that can contribute to the state of the art. But I’m not so arrogant as to assume I can accomplish that feat and certainly not as my first project in reinforcement learning. I’ll aim for something simple as my starting point. Got to crawl before I can walk, and all that.
Transferring reinforcement learning from a simulator to work in the real world is still a lot to tackle. So I’m going to look at a simulated world and stay within that simulated world while I learn the ropes. And before I can realistically think about contributing to algorithm advancements, I should get familiar with applying existing implementations of reinforcement learning. All of these new priorities turned my attention back to the game world of Unity ML-Agents.