ETOOBUSY 🚀 minimal blogging for the impatient
I’ve been interested into the Monte Carlo tree search algorithm lately.
The Monte Carlo tree search is an interesting algorithm that can be useful when trying to code an AI for a game (like e.g. board games).
The Wikipedia page does a good job describing the algorithm; to fix the ideas, though, I’m going to write a quick summary here, jotting down a few things that were not immediately clear at the beginning. This will hopefully come useful to others… hey future me!
One interesting thing about this algorithm is its relative youth. It seems to have surged in interest after AlphaGo adopted it within its gamut of algorithms… and a lot of the material seems to concentrate on how cool AlphaGo is instead of getting into the details of the algorithm. Or… my Web-Fu isn’t as strong as I wish.
Steps…. and phases
The algorithm is normally divided into four steps, which is fine and good for implementation.
On the other hand, I think that there are two phases, based on how it works in general. The basic idea is that we don’t know much about where we are in a specific situation, and we want to learn more about our options. To do this, we need to study.
What we have learned so far is kept in a tree of our current knowledge. At the beginning it will not be very accurate - we hope that our knowledge will improve with more study.
So, I really see two phases in MCTS:
- Deterministic: the currently known tree of decisions is traversed according to a deterministic algoritm to figure out what part it’s better to study more. The general spirit here is that we might feel that going down one path might be the right way, but yet we keep enough skepticism to also probe ways that we consider less rewarding, just in case we had some bad luck in studying those alternatives. This phase has a single implementation step called Selection.
- Stochastic: at a certain point, we will reach the frontier of our
current knowledge. We need to study more! To do this, we adopt a Monte
Carlo strategy, going randomly until we hit some result (e.g. one of the
players wins) and taking note of what happened (i.e. “learning”
something). This phase has three steps:
- Expansion: when we are at the frontier (i.e. in a leaf of our current tree of knowledge), we might need to decide what to explore next. To do this, we expand the node adding to it a new leaf for each possible move that would be allowed in that node’s state. This step might not be necessary all times, especially if we hit a leaf node for which we have no previous knowledge at all (i.e. we cannot apply the deterministic part to it);
- Simulation: this is the random study we discussed above, at the core of the stochastic part. As the [Wikipedia article][Monte Carlo tree simulation] points out, this step might also be called Rollout or Playout.
- Backpropagation: this is the learning part, where we go back in our tree of knowledge recording what happened in this particular simulation.
This is really it in a nutshell!
Number of players
I had to struggle a bit with understanding how thing worked with multiple players, although in hindsight I was probably tired when I read about it in the first place because the Wikipedia post is actually clear to this regard.
The algorithm can be adapated to whatever number of players, even solitaires. As I understood it, the only part where the number of different players actually matters is during the last step, i.e. the backpropagation where we learn some information about our simulation.
In particular, assuming that we are always able to associate a node in the tree with one single player that has to decide a move:
- the specific node will have its win counts incremented by 1 if and only if the simulation led to the victory of the associated player;
- the specific node will have its win counts incremented by $1 / N$ (where $N$ is the number of players) in case of a draw in the simulation.
Points-assignment refinements might chage on a game-to-game situation (e.g. if a 3-players game might yield a shared victory between two of the three players), but the basic idea is the one above.
This adapts well to any number of players, and it also simplifies the selection step because each node will contain data as seen from the point of view of the player that has to place a move.
Yes, this took me a bit to understand 😅
This barely scratched the surface of the algorithm, I’d like to move towards some kind of implementation to learn it better. Until next time… stay safe!
I’ll leave this post with a useful link to an article I want to investigate more in depth: A survey of Monte Carlo tree search methods (2012), by Cameron Browne , Edward Powley , Daniel Whitehouse , Simon Lucas , Peter I. Cowling , Stephen Tavener , Diego Perez , Spyridon Samothrakis , Simon Colton, et al. The article can be found in various places, I also keep a local copy here, mirrored from this copy on diego-perez.net.