BAI Fan

@HanqingWangAI Thank you for your reply. I will be always looking forward to your improving. Now, I am still studying your code and have made some progress. I have successfully...

@HanqingWangAI Dear author, I guess that 'train_comb_one_frame_add_action_all_reward_loss_details_failure_cases_reinforce_conflict_large_most_25_random_index_NN12_poly_2_channel_net17()' is the code in your paper, because it has 25 objects, 64*64 map size and LSTM. And I can use 'test_mover_64_net(env, net, sess)'...

@HanqingWangAI Dear author, Thank you for your new code. It is very helpful to me. Now I use PPO to train the policy, but it does not work. I have...

@HanqingWangAI Dear author, thank you very much! I create a new repo, you can find code and curves from https://github.com/baifanxxx/SceneMover-New If you have time and interested in it, you can...

@HanqingWangAI Dear author, I'm sorry to disturb you again. what do you think about my PPO code? By the way, I noticed that there is an AC algorithm in your...

@HanqingWangAI Thank you for your reply. your a2c curve is good. In fact, I have trained my PPO for a long time, but there has been no good results, and...

Hi Wang, Thank you again for your excellent work. I still hope you help me again. Regarding the A* trajectory problem, I think you are using the A* algorithm in...

Thank you for your suggestions, these are very interesting papers, I have selected an awesome one to add to the list.