it’s still not the case that we can train a straightforward neural net on winning and losing chess moves and have it generate winning moves. For AlphaGo, the Monte Carlo Tree Search was a major component of its architecture, and then any of the followup-systems was trained by pure self-play.
We also assessed variants of AlphaGo that evaluated positions using just the value network (λ = 0) or just rollouts (λ = 1) (see Fig. 4b). Even without rollouts AlphaGo exceeded the performance of all other Go programs, demonstrating that value networks provide a viable alternative to Monte Carlo evaluation in Go.
Even with just the SL-trained value network, it could play at a solid amateur level:
We evaluated the performance of the RL policy network in game play, sampling each move...from its output probability distribution over actions. When played head-to-head, the RL policy network won more than 80% of games against the SL policy network. We also tested against the strongest open-source Go program, Pachi14, a sophisticated Monte Carlo search program, ranked at 2 amateur dan on KGS, that executes 100,000 simulations per move. Using no search at all, the RL policy network won 85% of games against Pachi.
I may be misunderstanding this, but it sounds like the network that did nothing but get good at guessing the next move in professional games was able to play at roughly the same level as Pachi, which, according to DeepMind, had a rank of 2d.
Yeah, I mean, to be clear, I do definitely think you can train a neural network to somehow play chess via nothing but classification. I am not sure whether you could do it with a feed forward neural network, and it’s a bit unclear to me whether the neural networks from the 50s are the same thing as the neural networks from 2000s, but it does sure seem like you can just throw a magic category absorber at chess and then have it play OK chess.
My guess is modern networks are not meaningfully more complicated, and the difference to back then was indeed just scale and a few tweaks, but I am not super confident and haven’t looked much into the history here.
AlphaGo without the MCTS was still pretty strong:
Even with just the SL-trained value network, it could play at a solid amateur level:
I may be misunderstanding this, but it sounds like the network that did nothing but get good at guessing the next move in professional games was able to play at roughly the same level as Pachi, which, according to DeepMind, had a rank of 2d.
Yeah, I mean, to be clear, I do definitely think you can train a neural network to somehow play chess via nothing but classification. I am not sure whether you could do it with a feed forward neural network, and it’s a bit unclear to me whether the neural networks from the 50s are the same thing as the neural networks from 2000s, but it does sure seem like you can just throw a magic category absorber at chess and then have it play OK chess.
My guess is modern networks are not meaningfully more complicated, and the difference to back then was indeed just scale and a few tweaks, but I am not super confident and haven’t looked much into the history here.