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What happens when you take a strategy from academic research and pit it against modern empirical approaches? We found out by implementing the SmoothnessStrategy from a 2014 research paper and putting it through our benchmarking gauntlet.

The results were fascinating - and opened up entirely new directions for strategy development.

The Academic Foundation

While building our benchmarking framework, we discovered we had an unfinished implementation of a strategy based on the 2014 paper “An AI for 2048 - Part 4 Evaluation Functions.” The paper tested three core heuristics:

• Smoothness: Minimize differences between adjacent tiles
• Monotonicity: Prefer sorted rows and columns
• Empty Tiles: Maximize available space

The research found that smoothness was the strongest single heuristic, achieving 720 average score and reaching the 1024 tile - significantly outperforming monotonicity (420) and empty tiles (390).

Our Implementation

We implemented the research findings as a weighted evaluation function:

// Weights based on academic research - smoothness is primary
private weights = {
  smoothness: 0.5,   // 50% - research shows this is most important
  monotonicity: 0.3, // 30% - helps with organization
  emptyTiles: 0.15,  // 15% - survival factor
  cornerBonus: 0.05  // 5% - high tiles in corners
};

Smoothness Calculation

The core insight is measuring how “smooth” the board is by minimizing tile value differences:

private calculateSmoothness(grid: Grid): number {
  let smoothness = 0;
  
  for (let r = 0; r < 4; r++) {
    for (let c = 0; c < 4; c++) {
      if (grid[r][c]) {
        const currentValue = grid[r][c]!;
        
        // Check right neighbor
        if (c < 3 && grid[r][c + 1]) {
          const diff = Math.abs(currentValue - grid[r][c + 1]!);
          smoothness -= diff; // Subtract because we want smaller differences
        }
        
        // Check down neighbor  
        if (r < 3 && grid[r + 1][c]) {
          const diff = Math.abs(currentValue - grid[r + 1][c]!);
          smoothness -= diff;
        }
      }
    }
  }
  
  return smoothness;
}

The strategy evaluates each possible move and chooses the one that optimizes the weighted combination of all heuristics.

The Benchmark Results

Running our comprehensive 100-game benchmark revealed surprising results:

Performance Rankings:

Strategy	Avg Score	Speed	Type	Achievement
Greedy	3,079	1.99ms	Empirical	🏆 Still champion
Expectimax	2,365	593ms	Empirical	🧠 Sophisticated
Smoothness Master	2,142	2.41ms	Research	🔬 Academic approach
Corner Master	1,542	0.23ms	Empirical	⚡ Speed demon
Snake Builder	1,241	0.54ms	Empirical	🐍 Needs work

Key Findings:

1. Research Exceeded Expectations
Our SmoothnessStrategy achieved 2,142 average score - nearly 3x better than the original paper’s 720! This suggests significant improvements from:

• Better move simulation implementation
• Modern game engine optimizations
• Refined weight tuning

2. Empirical Still Leads
Despite solid performance, our simple Greedy strategy outperformed the research approach by 44% (3,079 vs 2,142). Sometimes simple heuristics beat complex weighted combinations.

3. Speed vs Intelligence Tradeoff
SmoothnessStrategy runs at 2.41ms per game - reasonably fast while being more thoughtful than pure greed. Compare to Expectimax’s 593ms for only 10% better performance.

4. The Winning Problem
Here’s the sobering reality: none of our strategies actually win the game. Across 600 total games (100 per strategy), we achieved exactly 0 wins - zero games reached the 2048 tile.

Our best achievements:

• Greedy: Reached 1024 once, 512 nine times
• SmoothnessStrategy: Reached 512 six times
• Expectimax: Reached 512 once

This reveals a fundamental gap between “playing well” and “winning consistently.”

What Made the Difference?

The dramatic improvement over the original research (2,142 vs 720) likely came from:

Implementation Quality:

• Proper move simulation vs placeholder logic
• Accurate smoothness calculations
• Optimized evaluation functions

Modern Context:

• Better random number generation
• More robust game state handling
• Faster computational cycles allowing deeper evaluation

Weight Refinement:

• The 50/30/15/5 weight distribution appears well-tuned
• Corner bonus addition helps with late-game positioning
• Balanced approach prevents over-optimization

The Winning Challenge

Our 0% win rate across all strategies highlights a critical issue: we’re optimizing for the wrong metric. All our strategies focus on maximizing score, but the actual goal is reaching 2048.

This suggests several fundamental problems:

1. Short-Term Thinking: Strategies prioritize immediate gains over long-term positioning needed for 2048.

2. Risk Aversion: Conservative play that maximizes average score might not take the risks needed for the big win.

3. Endgame Weakness: Getting from 1024 to 2048 requires different tactics than early/mid-game optimization.

4. Evaluation Mismatch: Our heuristics reward “good-looking” boards rather than win probability.

Ideas for Improvement

The research foundation and winning challenge open exciting possibilities:

1. Dynamic Weight Adjustment

// Adapt weights based on game phase
private getWeights(moveCount: number, maxTile: number) {
  if (moveCount < 50) {
    // Early game: prioritize space and smoothness
    return { smoothness: 0.4, emptyTiles: 0.4, monotonicity: 0.2 };
  } else if (maxTile >= 256) {
    // Late game: focus on organization  
    return { monotonicity: 0.5, smoothness: 0.3, cornerBonus: 0.2 };
  }
  // Default research weights
  return this.weights;
}

2. Multi-Depth Smoothness

Instead of only checking adjacent tiles, evaluate smoothness patterns at multiple scales:

// Check 2-tile and 3-tile smoothness patterns
private calculateMultiScaleSmoothness(grid: Grid): number {
  let score = 0;
  
  // Adjacent smoothness (current)
  score += this.calculateSmoothness(grid) * 0.6;
  
  // 2-tile gap smoothness  
  score += this.calculateGapSmoothness(grid, 2) * 0.3;
  
  // 3-tile gap smoothness
  score += this.calculateGapSmoothness(grid, 3) * 0.1;
  
  return score;
}

3. Learning-Enhanced Research

Combine research heuristics with learned patterns:

// Use research weights as baseline, adjust based on observed outcomes
private adaptiveWeights = {
  baseline: { smoothness: 0.5, monotonicity: 0.3, emptyTiles: 0.15 },
  learned: { /* weights adjusted from successful games */ },
  confidence: 0.0 // How much to trust learned vs research
};

4. Hybrid Greedy-Smoothness

Since Greedy performs so well, create a hybrid approach:

// Combine immediate merge potential with smoothness
private evaluateHybrid(gameState: GameState, move: Direction): number {
  const greedyScore = this.countImmediateMerges(gameState, move) * 100;
  const smoothnessScore = this.evaluateSmoothness(gameState, move);
  
  // Weight immediate gains vs long-term board quality
  return greedyScore * 0.7 + smoothnessScore * 0.3;
}

5. Win-Oriented Evaluation

Redesign evaluation functions to prioritize win probability over score:

private evaluateWinPotential(gameState: GameState): number {
  const maxTile = this.getMaxTile(gameState.grid);
  const emptyTiles = this.countEmptyTiles(gameState.grid);
  
  // Heavily weight positions that could lead to 2048
  if (maxTile >= 1024) {
    // Endgame: focus on creating 2048 opportunity
    return this.evaluateEndgamePosition(gameState) * 1000;
  } else if (maxTile >= 512) {
    // Late game: setup for 1024 merger
    return this.evaluateLateGamePosition(gameState) * 100;
  } else {
    // Early/mid game: build foundation
    return this.evaluateFoundationBuilding(gameState);
  }
}

6. Risk-Taking Strategies

Develop strategies willing to sacrifice score for win probability:

// Sometimes take risky moves that could lead to wins
private evaluateRiskyMoves(gameState: GameState): Direction | null {
  const conservativeMove = this.getBestScoreMove(gameState);
  const riskyMove = this.getBestWinProbabilityMove(gameState);
  
  // In endgame, prefer risky moves that could create 2048
  if (this.isEndgame(gameState)) {
    return riskyMove;
  }
  
  return conservativeMove;
}

Research vs Reality Lessons

This experiment revealed several insights about academic research in practice:

Research Provides Direction: The paper’s identification of smoothness as a key heuristic was spot-on, even if the implementation details needed work.

Implementation Matters: The gap between 720 and 2,142 shows how much difference proper implementation makes.

Simple Can Win: Sometimes a straightforward greedy approach outperforms sophisticated weighted combinations.

Speed Matters: For interactive gameplay, SmoothnessStrategy’s 2.41ms is much more practical than Expectimax’s 593ms.

Hybrid Potential: The best future strategies likely combine research insights with empirical observations.

Future Experiments

The winning challenge and SmoothnessStrategy insights open up critical research directions:

1. Win-First Strategies: Develop strategies that prioritize reaching 2048 over maximizing score
2. Endgame Specialization: Create strategies specifically designed for 1024→2048 transitions
3. Risk/Reward Analysis: Study when to take risks vs play conservatively
4. Phase-Aware Evaluation: Adapt strategy based on game progress toward 2048
5. Monte Carlo Tree Search: Look ahead multiple moves to find win paths
6. Academic Strategy Survey: Implement more strategies from 2048 research papers
7. Machine Learning Enhancement: Train neural networks on games that actually reach 2048

Conclusion

This experiment revealed a fundamental tension between playing well and winning consistently. Our research-backed SmoothnessStrategy achieved solid performance (2,142 average score) but, like all our strategies, failed to actually beat the game.

The 0% win rate across 600 games is a wake-up call: we’ve been optimizing for the wrong metric. Academic research focuses on board evaluation and score maximization, but the real challenge is reliably reaching 2048.

Key takeaways:

Research Provides Foundation: The smoothness heuristic proved valuable, with our implementation achieving 3x the original paper’s performance.

Implementation Quality Matters: The gap between 720 and 2,142 shows how much proper implementation affects results.

Wrong Optimization Target: Maximizing average score ≠ maximizing win probability.

Endgame Gap: All strategies struggle with the crucial 1024→2048 transition.

Hybrid Future: The best strategies will likely combine research insights with win-focused evaluation.

The next phase of 2048 strategy development must shift from “how to play well” to “how to win consistently.” That’s a much harder problem - and a much more interesting one.

The data doesn’t lie, but it also reveals we’ve been asking the wrong questions.

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The complete SmoothnessStrategy implementation and benchmark results are available in our codebase. Try running npm run benchmark to see how it performs on your machine!