To enhance the iterative self-play training process and better leverage the GO-MCTS search during learning, the following improvements can be considered:
Incorporating Search Insights: Instead of using GO-MCTS solely for evaluation, integrate the search insights gained during the search process back into the training phase. This can involve updating the policy based on the search results and incorporating the learned strategies into the training data.
Adaptive Exploration: Implement adaptive exploration strategies that adjust the level of exploration during self-play based on the search outcomes. This can help the player explore more promising paths identified by the search algorithm.
Dynamic Hyperparameter Tuning: Dynamically adjust hyperparameters such as exploration rate, temperature, or search depth based on the performance of the player during self-play. This adaptive tuning can help the player adapt to different game scenarios and opponents.
Reward Shaping: Utilize reward shaping techniques to provide more informative feedback during training. By shaping the rewards based on the search outcomes or intermediate goals achieved during the search, the player can learn more effectively from the self-play experience.
Transfer Learning: Explore the use of transfer learning techniques to transfer knowledge gained from the search process to the training phase. By initializing the player with insights from the search, the training process can start from a more informed state.
By incorporating these enhancements, the iterative self-play training process can better leverage the insights gained from the GO-MCTS search, leading to more efficient learning and improved player performance.