In addition to transformers, several other generative models could be explored for the observation dynamics model in GO-MCTS:
Recurrent Neural Networks (RNNs): RNNs are well-suited for modeling sequential data and could be used to capture the dynamics of observations in a game. Their ability to maintain a memory of past observations can be beneficial in predicting future states.
Variational Autoencoders (VAEs): VAEs can learn a latent representation of the observation space, allowing for efficient generation of new observations. By incorporating VAEs into the generative model, GO-MCTS could benefit from the ability to sample diverse and realistic observation sequences.
Gaussian Processes (GPs): GPs are probabilistic models that can capture uncertainty in the observation dynamics. By using GPs to model the transitions between observations, GO-MCTS could make more informed decisions based on the uncertainty in the predictions.
Markov Decision Processes (MDPs): MDPs provide a formal framework for modeling sequential decision-making processes. By incorporating MDPs into the observation dynamics model, GO-MCTS could leverage the principles of reinforcement learning to improve its decision-making capabilities.
Graph Neural Networks (GNNs): GNNs are effective in modeling relational data and could be used to capture the complex interactions between different elements in the observation space. By incorporating GNNs into the generative model, GO-MCTS could better understand the underlying structure of the game.
Exploring these alternative generative models could provide valuable insights into the observation dynamics of the game and enhance the performance of GO-MCTS in various domains.