An AI agent developed to play Ms. Pac-Man by adopting a strategy formed by MCTS and a FSM.
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Updated
Jul 17, 2021 - C#
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.
AI implementation using monte carlo tree search (MCTS) for the Game of Amazons
An AI agent for the card game Coup that uses ISMCTS.
Lightweight, extensible, and fair multi- DNN manager for heterogeneous embedded devices.
Tic-tac-toe/"noughts & crosses" written in Clojure (CLI + deps). AI powered by Monte Carlo tree search algorithm
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.
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