使用langchain进行任务规划,构建子任务的会话场景资源,通过MCTS任务执行器,来让每个子任务通过在上下文中资源,通过自身反思探索来获取自身对问题的最优答案;这种方式依赖模型的对齐偏好,我们在每种偏好上设计了一个工程框架,来完成自我对不同答案的奖励进行采样策略
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Updated
May 6, 2025 - Jupyter Notebook
使用langchain进行任务规划,构建子任务的会话场景资源,通过MCTS任务执行器,来让每个子任务通过在上下文中资源,通过自身反思探索来获取自身对问题的最优答案;这种方式依赖模型的对齐偏好,我们在每种偏好上设计了一个工程框架,来完成自我对不同答案的奖励进行采样策略
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
AI implementation using monte carlo tree search (MCTS) for the Game of Amazons
An AI agent for the card game Coup that uses ISMCTS.
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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