This is the code for my paper 🔗 IMPROVING MONTE CARLO TREE SEARCH BY COMBINING RAVE AND QUALITY-BASED REWARDS ALGORITHMS.
Original repository: Authored by Kenny Young here and I further implemented the gui and some simulation algorithms.
This project is further optimized in here
Researches have been done in Urmia University of Technology.
- Masoud Masoumi Moghadam (Me 😎)
- Prof: Mohammad Pourmahmood Aghababa profile
- Prof: Jamshid Bagherzadeh profile
MONTE Carlo Tree Search (MCTS) is a method for finding optimal decisions in a given domain by taking random samples in the decision space and building a search tree according to the results. It has already had a profound impact on Artificial Intelligence (AI) approaches for domains that can be represented as trees of sequential decisions, particularly games and planning problems. In this project I used different simulation strategies to enhance the agent policy to explore the environment.
- OS: Windows and Ubuntu
- tkinter
- Numpy
Another implementation boosted with Cython and C wrappers can be found in here:
🔗 Monte Carlo Tree Search boosted with cython
You can 🏃 (run) program using this command:
python main.py
Also you can run tests for comparing two mcts-based algorithms against
each other using the playtest.py.
This one is highly recommended:
🔗 A Survey of Monte Carlo Tree Search Methods
- Upper Confidence Bounds (UCT)
- UCB1-Tuned
- Rapid Action Value Estimation (RAVE)
- Decisive Move
- Quality Based Rewards
- Pool RAVE
- Last Good Reply