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by akuroiwa • Uncategorized
A generic Monte Carlo Tree Search (MCTS) framework augmented by AI agents for enhanced game and ligand design simulations.
A generic MCTS engine enhanced with AI-driven policy pruning and value predictions.
Integrate game simulations for Shogi, Chess, or ligand generation within their workflows.
A tool server compatible with Gemini CLI to manage MCTS-based simulations.
MCTS-Gen provides a flexible MCTS engine that integrates AI agents to improve search efficiency via policy pruning and value predictions. It supports extensible game logic for various games like Shogi, Chess, and ligand generation, with optional dependencies for each. The framework is designed for seamless integration with AI agents such as the Gemini CLI, enabling advanced simulations and decision-making processes.