2048 Search-Policy Optimization

You inherit a weak Python policy that plays deterministic seeded 2048 games. Improve the policy using repeated experiments on the public seed suite; your submitted policy is replayed from scratch on sealed seeds from the same generator for scoring.

Hard Constraints

• Edit methods/main/policy.py; it must define choose_move(board) and return one of "UP", "DOWN", "LEFT", or "RIGHT".
• board is a tuple of four tuples containing tile values, with zero for an empty cell.
• The policy may use only Python's standard library and must be deterministic for a given board.
• methods/main/policy.py must not exceed 10 MB. Learned weights are allowed within that budget — embed them in the file, since nothing outside methods/main/ is available at grading time. selfcheck.py enforces the cap, and so does the grader; an oversized file scores zero.
• Do not modify game2048.py, evaluate.py, selfcheck.py, or the public seed file.
• CPU budget: 225 seconds of CPU per game, enforced as one pooled limit across the whole sealed suite by a single policy process. Spend it unevenly if you like, but exhausting the pool kills the process, and every game not yet played then scores zero. selfcheck.py applies the same per-game budget to the public suite and reports cpu_seconds_per_game.
• A single move must return within 5 seconds.
• A callback error, illegal return value, or per-move timeout ends that game.
• The final policy must be self-contained in methods/main/ and must not use files, subprocesses, network access, clocks, environment variables, or randomness.

What You Have

• methods/main/policy.py: the weak starting policy.
• game2048.py: the exact game rules, move order, tile spawning, and raw score logic.
• evaluate.py: the public evaluation harness.
• visible_seeds.json: the fixed public games used for comparable development scores.
• selfcheck.py: runs the current policy on every public game and reports mean raw game score, median score, maximum tile, and per-seed results.
• methods/experiment_log.md: record each comparable attempt and whether it was kept or reverted. Save evaluated policy snapshots and results under methods/versions/vN/.

What You Submit

Submit the complete methods/ directory. The selected policy must be located at methods/main/policy.py; experiment history and notes may remain under methods/.

How It Is Judged

Each game starts with two spawned tiles and follows standard 4-by-4 2048 merge rules. After every legal move, a 2 tile is spawned with probability 0.9 and a 4 tile with probability 0.1. The raw metric is the arithmetic mean of the standard merge score over the seed suite, and higher is better. The normalized score is a monotonic function of sealed game performance and is not shown to you; optimize raw score and cross-seed generalization.

The hidden seeds are disjoint from the public seeds but use the identical simulator, spawn distribution, board size, move budget, and scoring code.