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
Jan 20, 2025 - Python
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
This is work-in-progress (WIP) refactored implementation of "Retreival-guided Reinforcement Learning for Boolean Circuit Minimization" work published in ICLR 2024.
A Monte-Carlo Tree Search mathod that enables two agents interact and work together in the game of Pacman Capture the Flag.
Lightweight, extensible, and fair multi- DNN manager for heterogeneous embedded devices.
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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