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pengguo318 / FJSPDRL

The code and data will be published after accepting our paper
MIT License
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feature concatenation about the paper, and the output layer in the actor network #6

Open Elfits opened 2 years ago

Elfits commented 2 years ago

Hi! Impressive work! In the paper, I found that the input for decoders is the concatenation of three components (see equations 3 and 4). However, it shows to be two instead of three in the code (actor_critic.py, line 178, concateFea = torch.cat((candidate_feature, h_pooled_repeated), dim=-1)). Did I miss the last component?

Another question, why did you use batch size for the number of nodes in the encoder part? (I found this in actor_critic.py, line 101, where the code is self.actor = MLPActor(3, hidden_dim*2, hidden_dim, 1).to(device) ). Here the output of the actor is 1, which means the last layer only has one neuron. Why not use a longer last layer for all the possible actions?

Lei-Kun commented 2 years ago

Hello, you can download the latest version of the code at: https://github.com/leikun-starting/End-to-end-DRL-for-FJSP

Lei-Kun commented 2 years ago

About the actor and critic network, please refer to Actor1.py.