Monte-Carlo Tree Search is a heuristic search algorithm used mainly for decision making processes.
In this project, we explored the use of Monte-Carlo Tree Search in many challenging environments, both as a real-time agent, and in the learning phase.
How will the vanilla algorithm contend with OpenAI Gym's challenging environments?
What heuristics and optimizations can we apply to bolster the performance?
Through testing, analysis and inspiration from academic papers, we devised new high-scoring algorithms for various real-time envrionments, and created a Game of Go playing agent, using a clever use of Monte-Carlo Tree Search in the learning phase, along with Convolutional Neural Networks.
In the end, we have shown that Monte-Carlo Tree Search is a powerful algorithm for many diverse tasks.
