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Reward delay is not very significant in this task, since the task is episodic and fully observable, and there is no time preference, thus you can just play a game to completion without updating and then assign the final reward to all the positions.
In more general reinforcement learning settings, where you want to update your policy during the execution, you have to use some kind of temporal difference learning method, which is further complicated if the world states are not fully observable.
Credit assignment is taken care of by backpropagation, as usual in neural networks. I don't know why RaelwayScot brought it up, unless they meant something else.
I meant that for AI we will possibly require high-level credit assignment, e.g. experiences of regret like "I should be more careful in these kinds of situations", or the realization that one particular strategy out of the entire sequence of moves worked out really nicely. Instead it penalizes/enforces all moves of one game equally, which is potentially a much slower learning process. It turns out playing Go can be solved without much structure for the credit assignment processes, hence I said the problem is non-existent, i.e. there wasn't even need to consider it and further our understanding of RL techniques.
Agreed, with the caveat that this is a stochastic object, and thus not a fully simple problem. (Even if I knew all possible branches of the game tree that originated in a particular state, I would need to know how likely any of those branches are to be realized in order to determine the current value of that state.)
Well, the value of a state is defined assuming that the optimal policy is used for all the following actions. For tabular RL you can actually prove that the updates converge to the optimal value function/policy function (under some conditions). If NN are used you don't have any convergence guarantees, but in practice the people at DeepMind are able to make it work, and this particular scenario (perfect observability, determinism and short episodes) is simpler than, for instance that of the Atari DQN agent.