This is a minimalist implementation of the game 2048. The aim is to have a high-performance backbone. So there aren't any visual components to play the game.
The board is represented by a UInt64 value.
I did not use any RL environment framework. But here's how you can play with it.
using Game2048Core: initbboard, add_tile, move, left, right, up, down, randmove, simulate_bb
import Game2048Core as g
# obtain a new board with 2 tiles populated
board = initbboard()
# you can move left right up or down
old_board = board
new_board = move(board, g.left)
if board != old_board
# this will add a new tile on the board
board = add_tile(new_board)
end
# make a random move
randmove(board)
# simulate the game til the end using purely random moves
simulate_bb(board)4×4 Matrix{Int8}:
1 2 4 1
4 5 1 3
3 7 2 1
1 3 4 2
using BenchmarkTools
b = initbboard()
@benchmark move($b, $left)BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
Range (min … max): 3.400 ns … 46.900 ns ┊ GC (min … max): 0.00% … 0.00%
Time (median): 3.400 ns ┊ GC (median): 0.00%
Time (mean ± σ): 3.480 ns ± 0.708 ns ┊ GC (mean ± σ): 0.00% ± 0.00%
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3.4 ns Histogram: frequency by time 3.6 ns <
Memory estimate: 0 bytes, allocs estimate: 0.
@benchmark move($b, $right)BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
Range (min … max): 2.700 ns … 29.200 ns ┊ GC (min … max): 0.00% … 0.00%
Time (median): 2.800 ns ┊ GC (median): 0.00%
Time (mean ± σ): 2.796 ns ± 0.552 ns ┊ GC (mean ± σ): 0.00% ± 0.00%
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2.7 ns Histogram: log(frequency) by time 3 ns <
Memory estimate: 0 bytes, allocs estimate: 0.
@benchmark move($b, $up)BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
Range (min … max): 4.500 ns … 38.900 ns ┊ GC (min … max): 0.00% … 0.00%
Time (median): 4.600 ns ┊ GC (median): 0.00%
Time (mean ± σ): 4.630 ns ± 0.833 ns ┊ GC (mean ± σ): 0.00% ± 0.00%
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4.5 ns Histogram: log(frequency) by time 5 ns <
Memory estimate: 0 bytes, allocs estimate: 0.
@benchmark move($b, $down)BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
Range (min … max): 4.800 ns … 38.800 ns ┊ GC (min … max): 0.00% … 0.00%
Time (median): 4.800 ns ┊ GC (median): 0.00%
Time (mean ± σ): 4.872 ns ± 0.913 ns ┊ GC (mean ± σ): 0.00% ± 0.00%
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4.8 ns Histogram: log(frequency) by time 5.1 ns <
Memory estimate: 0 bytes, allocs estimate: 0.
@benchmark simulate_bb($board)BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 2.800 μs … 3.577 ms ┊ GC (min … max): 0.00% … 98.69
%
Time (median): 11.200 μs ┊ GC (median): 0.00%
Time (mean ± σ): 12.377 μs ± 36.046 μs ┊ GC (mean ± σ): 2.85% ± 0.99
%
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2.8 μs Histogram: frequency by time 28 μs <
Memory estimate: 2.58 KiB, allocs estimate: 33.
@benchmark simulate_bb()BenchmarkTools.Trial: 10000 samples with 3 evaluations.
Range (min … max): 4.900 μs … 1.096 ms ┊ GC (min … max): 0.00% … 98.41
%
Time (median): 10.350 μs ┊ GC (median): 0.00%
Time (mean ± σ): 11.232 μs ± 21.695 μs ┊ GC (mean ± σ): 3.80% ± 1.97
%
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4.9 μs Histogram: frequency by time 19 μs <
Memory estimate: 4.53 KiB, allocs estimate: 58.
@benchmark randmove($board)BenchmarkTools.Trial: 10000 samples with 990 evaluations.
Range (min … max): 46.566 ns … 3.521 μs ┊ GC (min … max): 0.00% … 97.9
2%
Time (median): 47.879 ns ┊ GC (median): 0.00%
Time (mean ± σ): 56.294 ns ± 116.969 ns ┊ GC (mean ± σ): 7.40% ± 3.5
2%
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46.6 ns Histogram: log(frequency) by time 96.8 ns <
Memory estimate: 80 bytes, allocs estimate: 1.