Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates
This work addresses the need for interpretable strategies in AI game-playing, offering an incremental improvement over existing methods by reducing exploration for better clarity.
The paper tackles the problem of learning non-exploratory policies for interpretable strategies in board games, proposing a novel objective function using policy gradients and MCTS value estimates, and empirically evaluates these policies across various games.
In recent years, state-of-the-art game-playing agents often involve policies that are trained in self-playing processes where Monte Carlo tree search (MCTS) algorithms and trained policies iteratively improve each other. The strongest results have been obtained when policies are trained to mimic the search behaviour of MCTS by minimising a cross-entropy loss. Because MCTS, by design, includes an element of exploration, policies trained in this manner are also likely to exhibit a similar extent of exploration. In this paper, we are interested in learning policies for a project with future goals including the extraction of interpretable strategies, rather than state-of-the-art game-playing performance. For these goals, we argue that such an extent of exploration is undesirable, and we propose a novel objective function for training policies that are not exploratory. We derive a policy gradient expression for maximising this objective function, which can be estimated using MCTS value estimates, rather than MCTS visit counts. We empirically evaluate various properties of resulting policies, in a variety of board games.