Actually, I’d say your approach is a pretty awesome solution, with almost zero-run time cost compared to any sort of pathfinding. If you need it to generalise to arbitrary maps, you could use any pathfinding algorithm – breadth-first search is simple to implement, for example – and use that to calculate which directions to encode … Read more
In the neural network terminology: one epoch = one forward pass and one backward pass of all the training examples batch size = the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you’ll need. number of iterations = number of passes, each pass using [batch size] … Read more
I think that biases are almost always helpful. In effect, a bias value allows you to shift the activation function to the left or right, which may be critical for successful learning. It might help to look at a simple example. Consider this 1-input, 1-output network that has no bias: The output of the network … Read more
I developed a 2048 AI using expectimax optimization, instead of the minimax search used by @ovolve’s algorithm. The AI simply performs maximization over all possible moves, followed by expectation over all possible tile spawns (weighted by the probability of the tiles, i.e. 10% for a 4 and 90% for a 2). As far as I’m … Read more