YeWR
commented
2 years ago We use nn.LSTM to handle the prediction of the value prefix. And the input/output of nn.LSTM contains the final hidden states and the final cell states.
For detailed usage, please refer to https://pytorch.org/docs/1.9.1/generated/torch.nn.LSTM.html.
But they are tuple. The xxx_pools in mcts.py are simple structured databases for saving the corresponding data. For better indexing, the tuple is split into two arrays, namely c_pool and h_pool.
Hope this can help you:)
sekv
Hi, In mcts.py code line 35-36, what does reward_hidden_c and reward_hidden_h mean? ( what is c and h short for?) why reward_hidden_c_pool = [reward_hidden_roots[0]] and reward_hidden_h_pool = [reward_hidden_roots[1]]. I find it difficult to understand the code, could you give some comments. Many thanks!