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Thanks, karpathy! I did submit your JS implementation to HN when I came across it: https://news.ycombinator.com/item?id=9108738 https://news.ycombinator.com/it

Thanks, karpathy! I did submit your JS implementation to HN when I came across it: https://news.ycombinator.com/item?id=9108738 https://news.ycombinator.com/item?id=9108738 Monte Carlo Tree Search could be the missing link. In other words, use DQN to model the world and map actions to a value function. Then use playouts and backpropogation of action tree results to find tactics. Of course, it does not solve the big question: how to model "memories" and "inferences"? Indeed, very exciting times for AI/ML!

zhanwei 12y ago
We need a model to use Monte Carlo Tree Search... which is missing in this approach since it uses model-free reinforcement learning. Unless the deep convolution net can extract some latent features as state, it would be impossible to do planning on top of it.