自定义俄罗斯方块放置算法咨询：AI无消行类俄模拟实现问题

Great question—building a Tetris-like system where lines don’t clear and you’re optimizing for a tightly packed, low-gap matrix with genetic algorithms is a clever twist on the classic game. Let’s break down the key implementation pieces you’ll need to tackle:

First, you’ll need to build the foundational mechanics to simulate block placement reliably:

• Matrix Representation: Use a 2D array (e.g., matrix[row][col]) where each cell is marked as occupied (1) or empty (0). Define fixed dimensions upfront (like 10x20 for a standard Tetris-style grid).
• Block Shape & Rotation: Represent each block type as a set of coordinate offsets relative to a pivot point. For example, an I-block could be [(0,0), (1,0), (2,0), (3,0)]. To handle rotation, apply a 90-degree transformation to each offset (e.g., (x, y) → (y, -x)), then adjust the pivot to keep the block centered and avoid out-of-bounds issues.
• Valid Placement Enumeration: For each rotated block variant, iterate through all possible pivot positions in the matrix where the block fits—meaning all its cells stay within grid bounds and target cells are empty. Store each valid (position, rotation) pair as a possible action for the current block.

Your GA will evolve sequences of placement decisions to maximize matrix packing. Here’s how to structure each component:

2.1 Population Initialization #

Each individual in the population represents a sequence of valid placement decisions for a fixed set of incoming blocks. For example, if simulating 20 blocks, each individual is a list of 20 (position, rotation) pairs, each tied to a block in the sequence. Initialize the population randomly by picking valid actions for each block step.

2.2 Fitness Function (Fixed-Weight Evaluation) #

This is the heart of your system—define a weighted sum of metrics that reward tight, gap-free packing. Adjust weights based on your priorities:

• Total Filled Cells: Weight w1 (positive) – rewards maximizing occupied space.
• Gap Count: Weight w2 (negative) – penalizes empty cells, especially isolated or hard-to-fill gaps.
• Column Height Variance: Weight w3 (negative) – penalizes large differences between column heights (reduces uneven stacks that trap gaps).
• Pit Count: Weight w4 (negative) – penalizes "pits" (columns shorter than both adjacent columns, which are nearly impossible to fill later).

Example fitness formula:

fitness = w1 * total_filled + w2 * gap_count + w3 * height_variance + w4 * pit_count

Start with baseline weights (e.g., w1=10, w2=-5, w3=-2, w4=-3) and tweak through testing to get the best packing results.

2.3 Selection #

Choose high-fitness individuals to breed the next generation:

• Tournament Selection: Pick a random subset of 3-5 individuals and select the one with the highest fitness. Repeat until you have enough parents. This is more reliable than roulette wheel selection, which can over-favor dominant individuals.
• Elitism: Keep the top 5-10% of individuals from the current generation intact for the next—this preserves your best solutions.

2.4 Crossover #

Combine parent sequences to create offspring:

• Single-Point Crossover: Pick a random index k in the decision sequence. Take the first k decisions from Parent A and the remaining from Parent B to form a new offspring. Ensure all combined decisions are still valid (or repair invalid ones by swapping in a random valid action for that block step).

2.5 Mutation #

Introduce small random changes to maintain genetic diversity:

• For a random decision in the sequence, replace it with a different valid (position, rotation) pair for that block. Keep the mutation rate low (1-5% per decision) to avoid disrupting good sequences.

• Prune Invalid Actions Early: When enumerating possible placements, skip positions that seal off sections of the matrix (e.g., a block that creates a closed pocket with no way to fill it later). This reduces the search space and speeds up evaluation.
• Cache Fitness Calculations: If multiple individuals lead to the same matrix state, cache the fitness score to avoid recalculating it repeatedly.
• Termination Conditions: Stop the GA when:
  • The optimal individual’s fitness hasn’t improved for 10+ generations.
  • The matrix is completely filled.
  • You’ve reached a maximum number of generations (e.g., 100).

1. Build the basic simulation: Block generation, rotation, placement, and matrix state tracking. Test this manually first to ensure it works as expected.
2. Implement the GA core: Population management, fitness calculation, selection, crossover, and mutation.
3. Integrate the two: Feed block sequences through the GA, simulate each individual’s decisions, compute fitness, and evolve the population.
4. Tune weights and parameters: Adjust fitness weights, population size, mutation rate, etc., to refine packing performance.

内容的提问来源于stack exchange，提问作者vajnaivik