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Building AI agents for 2048 isn’t just about implementing random heuristics and hoping for the best. We dove deep into the academic literature to understand what actually works.

The Problem with Traditional Approaches

Most 2048 solvers use basic greedy strategies or simple heuristics. But the game’s complexity demands more sophisticated approaches that can reason about future states and adapt to changing board configurations.

What the Research Shows

After reviewing papers on expectimax algorithms, Monte Carlo tree search, and neural network approaches, we identified several key strategies:

1. Smoothness Strategy

The smoothness heuristic rewards boards where adjacent tiles have similar values, reducing the likelihood of getting “stuck” with incompatible tiles.

function calculateSmoothness(board: number[][]): number {
  let smoothness = 0;
  for (let row = 0; row < 4; row++) {
    for (let col = 0; col < 4; col++) {
      if (board[row][col] !== 0) {
        // Check adjacent tiles
        const currentValue = Math.log2(board[row][col]);
        // Calculate differences with neighbors...
      }
    }
  }
  return smoothness;
}

2. Corner Strategy

Keeping the highest value tile in a corner and building monotonic sequences leads to more stable board states.

3. Expectimax with Pruning

Using expectimax search with alpha-beta pruning allows the agent to look several moves ahead while remaining computationally efficient.

Implementation Results

Our research-backed agents consistently achieve higher scores than naive approaches:

• Greedy Agent: Average score ~8,000
• Corner Strategy: Average score ~15,000
• Smoothness + Expectimax: Average score ~35,000+

What’s Next

We’re exploring neural network approaches that can learn optimal strategies through self-play, potentially surpassing even the best handcrafted heuristics.

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This post is part of our ongoing series on building AI agents for 2048. Check out the live demo to see these strategies in action.