#Monte Carlo Tree Search

Alpha Zero General provides an adaptable implementation of self-play reinforcement learning based on the AlphaGo Zero model. It is compatible with any two-player turn-based game and supports various deep learning frameworks, making it a useful asset for developers. The project includes examples for games like Othello, GoBang, and TicTacToe using PyTorch and Keras. Its design allows for easy customization through subclassing game and neural network templates. Key features include a training loop, Monte Carlo Tree Search, and flexible neural network parameter settings. Setup can be streamlined with nvidia-docker for a Jupyter environment.

Investigate the approach of verified code generation utilizing Monte Carlo Tree Search and language models. This method employs verifiers at each stage, integrating tools such as Dafny, Coq, Lean, Scala, and Rust. It allows less advanced models to perform competitively against more robust ones, complete with comprehensive setup and execution guides. The project's reliance on GPU and its application across various languages ensure the synthesis of effective multi-step programs with precise verification, suited for developers focused on code validation and synthesis.