基于langchain设计的智能体任务,包含规划会话场景资源,构建子任务,任务执行器包含(MCTS)
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Updated
Sep 12, 2025 - Jupyter Notebook
基于langchain设计的智能体任务,包含规划会话场景资源,构建子任务,任务执行器包含(MCTS)
An AI agent developed to play Ms. Pac-Man by adopting a strategy formed by MCTS and a FSM.
MiniMax with Alpha-Beta pruning and Monte-Carlo Tree Search implementations for the board game Hex
A Monte-Carlo Tree Search mathod that enables two agents interact and work together in the game of Pacman Capture the Flag.
This is work-in-progress (WIP) refactored implementation of "Retreival-guided Reinforcement Learning for Boolean Circuit Minimization" work published in ICLR 2024.
Adaptive Advanced Tree Search function designed for OpenWeb-UI
Every LLM invocation is wrapped with a Monte Carlo Tree Search (MCTS) pipeline. Served as a OpenAI compatible API server.
Lightweight, extensible, and fair multi- DNN manager for heterogeneous embedded devices.
Forecasting MCTS Variant Outcomes Across Board Games
An AI agent for the card game Coup that uses ISMCTS.
AI implementation using monte carlo tree search (MCTS) for the Game of Amazons
A Hex board game with a customizable Monte Carlo Tree Search (MCTS) agent with optional leaf parallelization in C++14. Includes a logging functionality for MCTS insights.
Using reinforcement learning to play games.
Tic-tac-toe/"noughts & crosses" written in Clojure (CLI + deps). AI powered by Monte Carlo tree search algorithm
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