The Brilliant Idea Behind Monte Carlo Tree Search
--
Before you read this article: Have you read my previous article on search algorithms?
In the world of complex searching algorithms, we often find ourselves navigating between two distinct ends of the spectrum: simplicity and complexity.
Most notably, (extremely) simple algorithms can be characterized as below:
- Breadth-first: exploring each level of nodes one by one
- Depth-first: exploiting a solution as long as possible
The problem is that both simple approaches operate in a rigid manner, without having any adjustable parameters.
Now, consider the converse — a sophisticated algorithm that employs a finely tuned blend of intelligent heuristics and hyperparameters. Below is the list of required arguments for Simulated Annealing:
“In order to apply the simulated annealing method to a specific problem, one must specify the following parameters: the state space, the energy (goal) function
E()
, the candidate generator procedureneighbour()
, the acceptance probability functionP()
, and the annealing scheduletemperature()
AND initial temperatureinit_temp
“
The unique strength of this approach lies in its adaptability. By fine-tuning the individual arguments assigned to each heuristic, such complex algorithms can be tailored to address specific scenarios effectively.
However, this adaptability comes at a cost — a lack of generalization. The algorithm’s fine-tuning may perform exceptionally well in one context but struggle in another.
This trade-off between adaptability and generalization presents its own set of challenges and potential problems. Explainability, for example, may be an issue: can you explain “why the energy function is chosen logarithmic” or “how the temperature relates to the…