Here's a high-level overview of how you can implement MCTS for UTTT:
Define the Game Rules:
Start by defining the rules of UTTT. Understand the game mechanics, legal moves, and how the game state transitions from one position to another.
Create a UTTT Simulator:
Build a UTTT simulator that can represent the game state and allow you to make moves, check for wins, and determine valid moves.
MCTS Components:
Implement the core components of MCTS, which include the following:
- Node Structure: Create a node structure that represents each state in the search tree. Each node should store information such as the number of visits, the total reward, and the possible actions.
- Selection: Develop a selection strategy (e.g., Upper Confidence Bound) to choose nodes in the tree to explore further.
- Expansion: When a selected node has unexplored actions, expand the tree by creating child nodes for those actions.
- Simulation (Rollout): Simulate random playouts from a node to estimate the value of unexplored states. The rollout policy can be random or based on heuristics.
- Backpropagation: Update the statistics of nodes as you backpropagate the results of rollouts to their parent nodes.
UCT Algorithm:
Consider using the Upper Confidence Bound for Trees (UCT) algorithm, which is a widely used selection strategy within MCTS. UCT balances exploration and exploitation.
Search and Decision-Making:
Create a search loop that repeatedly selects nodes to expand, simulate, and update based on MCTS until a stopping criterion (e.g., time limit or a maximum number of iterations) is reached.
Integration with UTTT Environment:
Integrate your MCTS implementation with the UTTT environment, allowing it to interact with the game and make decisions based on the search results.
Tuning Parameters:
Experiment with the parameters of the MCTS algorithm, such as exploration constant, to fine-tune its performance.
Parallelization (Optional):
If computational resources allow, consider parallelizing MCTS to speed up the search process.
MCTS Variants:
Explore advanced MCTS variants like Monte Carlo Tree Search with Upper Confidence Bounds applied to Trees (MCT-UCT) or other enhancements that may improve performance.
Testing and Evaluation:
Test your MCTS-based UTTT AI against various opponents or strategies to evaluate its performance and iteratively refine your implementation.
Optimization (Optional):
If your MCTS implementation is running too slowly, consider optimization techniques to make the search process more efficient.
Debugging and Profiling:
Use debugging tools and profiling to identify and address issues in your implementation.
LINKS
Good GitHub Repos for coding MCTS:
MCTS install via pip:
Not too helpful Repos: