Don’t know anything about Lua or Torch, and not so much about machine learning. Little project to get going.

Torch is to Lua what Numpy is to python. Never done any lua before, although for a while it was the main language on the esp8266. Torch seems like a popular base for machine learning in competition with theano and tensorflow. Lua is like if python and javascript has a slightly retarded baby.

Thought I’d give a simple tic tac toe playing guy a go. The structure is play a bunch of totally random games, collect up all the winning games. Then the problem is a classification problem where the categories are the next move (1-9).

Then used the stock nn neural network package to learn on it. Had a tough time finding clear docs. I am unimpressed.

Then use trained neural network to play against the random component.

The win stats increased from ~28% to ~45% (with some fluctuations run to run of a couple percent). Not bad. Especially since going second is disadvantageous. Okay, as I wrote that I realized it’s easy to try flipping that. Going first the stats go from 59% to 69%.

Hmmm. Maybe I should look at draws?

Also, a smart strategy for the moves would be to use the suggested moves according to their rank, not using the top suggested move then if that is invalid using a random move.

math.randomseed(os.time()) function won(board,x) --diagonals if board[1][1] == x and board[2][2]==x and board[3][3] == x then return true end if board[1][3] == x and board[2][2]==x and board[3][1] == x then return true end --rows for i=1,3 do if board[i][3] == x and board[i][2]==x and board[i][1] == x then return true end end --columns for i=1,3 do if board[1][i] == x and board[2][i]==x and board[3][i] == x then return true end end return false end function full(board) for i=1,3 do for j=1,3 do if board[i][j] == '' then return false end end end return true end function mapBoardtoNum(board) newboard = {{},{},{}} for i=1,3 do for j=1,3 do if board[i][j] == 'x' then newboard[i][j] = 1 end if board[i][j] == '' then newboard[i][j] = 0 end if board[i][j] == 'o' then newboard[i][j] = -1 end end end return newboard end --[[ print(won({ {'x','',''}, {'x','o',''}, {'x','o',''}}, 'o')) ]] mymoves = {} myboards = {} wins = 0 gamenum = 10000 for k=1,gamenum do board = {{'','',''}, {'','',''}, {'','',''}} move = 'o' game = {} choices = {} turn = 1 while not won(board,'x') and not won(board,'o') and not full(board) do if move == 'x' then move = 'o' elseif move == 'o' then move = 'x' end repeat i = math.random(3) j = math.random(3) until board[i][j] == '' if move == 'x' then game[turn] = mapBoardtoNum(board) choices[turn] = i -1 + 3 * (j-1) +1 turn = turn + 1 end board[i][j] = move end if won(board,'x') then wins = wins +1 for i = 1,#game do table.insert(myboards, game[i]) table.insert(mymoves, choices[i]) end end end --print(mymoves) --print(#myboards) print('won ' .. wins ..' out of ' .. gamenum) training = {} --[[ training.data = torch.Tensor(myboards) training.labels = torch.Tensor(mymoves) training.size = function() return (#mymoves) end ]] training.size = function() return (#mymoves) end for i=1,training:size() do training[i] = {torch.Tensor(myboards[i]), torch.Tensor({mymoves[i]})} end ninputs = 9 nhiddens = 30 noutputs = 9 require 'nn' model = nn.Sequential() model:add(nn.Reshape(ninputs)) model:add(nn.Linear(ninputs,nhiddens)) model:add(nn.Tanh()) model:add(nn.Linear(nhiddens,noutputs)) model:add( nn.LogSoftMax() ) criterion = nn.ClassNLLCriterion() trainer = nn.StochasticGradient(model, criterion) trainer.learningRate = 0.01 trainer.maxIteration = 7 trainer:train(training) --[[ print(board) print(won(board,'o')) print(won(board,'x')) print(choices) print(game[1]) ]] board = { {'x','o',''}, {'x','o',''}, {'x','o',''} } logprobs= model:forward(torch.Tensor(mapBoardtoNum(board))) print(logprobs) max, pred =torch.max(logprobs,1) print(max) print(pred) --[[ -- Basic format { {'x','o',''}, {'x','o',''}, {'x','o',''} } ]] print('random won ' .. wins ..' out of ' .. gamenum) mymoves = {} myboards = {} wins = 0 for k=1,gamenum do board = {{'','',''}, {'','',''}, {'','',''}} move = 'o' game = {} choices = {} turn = 1 while not won(board,'x') and not won(board,'o') and not full(board) do --print('yo') if move == 'x' then move = 'o' repeat i = math.random(3) j = math.random(3) until board[i][j] == '' board[i][j] = move elseif move == 'o' then move = 'x' --print(board) --print(torch.Tensor(mapBoardtoNum(board))) local probs = model:forward(torch.Tensor(mapBoardtoNum(board))) maxs, pred = torch.max(probs,1) --i -1 + 3 * (j-1) +1 pred = pred - 1 i = pred % 3 + 1 j = (pred - pred%3) / 3 + 1 i = i[1] j = j[1] --print(i[1]) --print(j) --print(board) --print(board[i][j]) if board[i][j] == '' then board[i][j] = move else repeat i = math.random(3) j = math.random(3) until board[i][j] == '' board[i][j] = move end end end if won(board,'x') then wins = wins +1 end end print('learned won ' .. wins ..' out of ' .. gamenum)