You need a network file, and run the following command
make <name of game (lowercase)> EXE=<output path> EVALFILE=<path to network file>
to compile monty for the given game (chess, ataxx, shatranj).
The first version (0.1.0) used external data for value networks and self-generated policy data. The networks were then reset completely, and all future versions are trained exclusively on monty's own data, generated from scratch with uniform policy and material counting value.
Thanks to everyone at SWE as usual, in particular Cosmo (Viridithas) and Zuppa (Alexandria), for helping with data generation, and Plutie, for running an LTC tune.
| Version | Release Date | CCRL 40/15 | CCRL Blitz | CCRL FRC | Notes |
|---|---|---|---|---|---|
| 1.0.0 | 28th May 2024 | TBD | TBD | TBD | Fully Original Data |
| 0.1.0 | 26th March 2024 | - | - | 2974 | First Release |
Monte-Carlo Tree Search is broken down into 4 steps to build a tree.
To begin with, only the root node is in the tree.
- Selection of a node in the tree which has at least one unexplored child.
- Expansion to one of the unexplored children, and adding it to the tree.
- Simulation of the result of the game.
- Backpropogation of the result to the root.
Unfortunately, MCTS in its purest form (random selection and random simulation to the end of the game) is really bad.
Instead, selection is done via PUCT, a combination of a policy network which indicates the quality of the child nodes, and the PUCT formula to control exploration vs exploitation of these child nodes.
And simulation is replaced with a neural network evaluation, called the value network.