Weekly Status Reports

As is mentioned in our team status report for the week, we have transitioned our project away from Mancala and into the game Go instead. Fortunately, the division of labor remains relatively similar and I am still broadly responsible for the training of a reinforcement-learning engine that will eventually be used to give move suggestions and positional evaluations to our users.

Accordingly, almost all of the research I did last week is still applicable, as the same self-play techniques can be used, and, in fact, have been proven to work in the cases of  AlphaZero and MuZero. After making the transition to Go this week, I had to do a quick catch-up on the rules and gameplay, but after that, along with the two above-linked papers, the research phase of my project has come to a close.

Of course, with the design presentations coming up next week, a good amount of my time this week was devoted to preparing for that as well, and the rest was spent building the platform for the reinforcement learning. The current consensus for optimal Go engine creation is a combination of deep learning and Monte Carlo Tree Simulations (MCTS). MCTS works by using self-play to simulate many game paths given a certain position, and choosing the move providing the best overall outcome. I have started work on creating the framework to perform these simulations as quickly as possible (holding game state, allowing the candidate engine to make moves against itself and returning the new board, etc.).

With regards to classwork helping me prepare for this project, I think the two ECE classes that helped the most are 18213 and 18344. I have not taken any classes in reinforcement learning or machine learning in general, but the research I did in the Cylab with ECE Prof. Vyas Sekar certainly helped me a huge amount, both in the subject matter of the research (deep learning) and the experience of reading scholarly papers to fully understand techniques you are considering using. What 18213 and 18344 provided was the “correct” way of thinking about setting up my framework. I need my simulations to be as efficient as possible while also maintaining accuracy, and I need my system to be as robust as possible, as I will need to make frequent changes, tuning parameters, etc. These combined with the research papers read last week, and the two above papers are what influenced my portion of the design the most.

Finally, in the next week I plan to finish the Go simulation framework, and begin work on setting up the reinforcement learning architecture, to begin training in the week after. MCTS simulation is quite efficient, but with the distinct limit on computational resources I have, allocating proper time is vital.

This week I did a combination of research on reinforcement learning, and opponent/platform setup to enable the RL model training.

With regard to research, I want to understand as much as possible about reinforcement learning before I start the process of actually building a Mancala RL model. Through preliminary research our group decided that a self-play method of training would be best, so I read a number of papers and tutorials on both the theory of self-play RL and the logistics of putting it into practice in Python. A few of the resources I used are shown below:

OpenAI Self-Play RL

HuggingFace DeepRL

Provable Self-Play (PMLR)

Towards Data Science

Python Q-Learning

In order to train the self-play RL model, I must have a competent opponent for the model to start off playing against, before it can train against previous iterations of itself. If I choose too strong of a starting opponent, the model will not get enough positive reinforcement (as it will almost never win), and if I choose too weak of one the reverse is true. As such, we will start with a relatively simple minimax strategy that looks two “ply” (single player turns) into the future. However, to build this strategy, I need a platform for the game to be played on (so the RL model can play the minimax opponent). This week I started building this platform, programming all game rules and actions, and a framework where two separate players can interact on the same board.  I then implemented unit tests to make sure all game actions were functioning as they should. With this now in place, I have begun programming the minimax strategy itself. This means I am on schedule, and hopefully will have the minimax available to start training within the week.