During my research for Google DeepMind's Go-playing program Alpha Go and its successor Alpha Go Zero, I discovered that the system uses a clever pipeline and an interplay of blocks of both policy and value networks to play the game of Go in such a way, that it is able to outperform even the best players in the world. This is in particular remarkable, because the game of Go was considered to be unsolvable a few years ago. This success gained international attention and it was labeled as a breakthrough in the community of AI. It is also not a secret that the research team behind AlphaGo and AlphaGo Zero used lots of computation power to create such a sophisticated system.
But, since each board configuration is considered as a distinct state, where algorithms can be applied really well, and just consider AlphaGo Zero, which uses no prior knowledge and can figure out how the play the game of go from scratch, my question is the following:
Is there any way to state (theoretically) how the performance of AlphaGo would be in continuous action spaces (e.g. self-driving cars)?
