This is my final project for CPSC 474 at Yale (Fall 2022). We use reinforcement learning to train an agent to play the popular 2048 game (https://play2048.co/). The agent is implemented in Rust and uses an Expectimax algorithm with a transposition table and corner heuristic to find the best move to make at each step.
To run the solver, first install Rust (https://www.rust-lang.org/tools/install). Then, clone this repository and run the following command:
cargo run --release <depth>where <depth> is the depth of the search tree. We recommend starting with a depth of 3, and increasing if you want to see the solver take longer to find the best move.
Alternatively, you can run the precompiled solver with the following command:
./rust_solver <depth>Using a depth of 6, the solver has achieved a max tile of 16384 and a high score of 250,040. See high-score.PNG for a screenshot of the high score (before CLI improvements). This is a work in progress, and we hope to improve the solver to achieve even higher scores!