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View Code? Open in Web Editor NEWThe implementation of AAAI'22 paper "Multi-Agent Incentive Communication via Decentralized Teammate Modeling".
License: Apache License 2.0
The implementation of AAAI'22 paper "Multi-Agent Incentive Communication via Decentralized Teammate Modeling".
License: Apache License 2.0
Why maximize Mutual Information between a_{j} and latent variable z_{ij}?
Thank you for your awesome work!
I was trying to reproduce Fig. 5 in your AAAI paper, and I used the default parameters for the MAIC algorithm on map 27m_vs_30m with a tiny change (batch_size=16, buffer_size=16
due to my GPU resource limitation, seed=0; seed=1
), and I tried two sets of learning rates (lr=0.0005, critic_lr=0.0005
as in the default configs and lr=0.00025, critic_lr=0.00025
since I reduce the batch size).
But after 2M environment timesteps, I still got the winning rate of 0 for both training and testing for all the settings I mentioned above. The return median is somewhere between 3 and 5. So I was wondering if there is some discrepancy between the default parameters in this repository and the ones used in Fig. 5 in your paper. It would be great if you could kindly release those settings.
Thank you very much!
Hello, I have installed the Python environment according to the requirements.txt file, but when I run the following command:
python src/main.py --config=qmix --env-config=foraging
The following error occurred
How can I solve this mistake? is there any other environment to install?
Looking forward to your reply.
你好,最近在研读您的文章,有个问题想请教一下。文中提到:
exchange local observation /embedding
This informative communication scheme can reduce uncertainty for individual local decision-
making and induce implicit coordination. However, such a communication mechanism enlarges agents’ local policy
spaces and increases learning complexity,.
我想问下这里的 enlarges agents’ local policy spaces and increases learning complexity, 的 local policy spaces是指扩大local observation吗?
In q_learner.py line 112:
td_error = (chosen_action_qvals - targets.detach())
However, in the reference, I see, the td_error should be
td_error = (targets.detach() - chosen_action_qvals)
I think if you refer to the GitHub Repository Python MARL framework? Cause in this Repository, the td_error is also:
td_error = (chosen_action_qvals - targets.detach())
Hello, it's said in the paper that the experiment on SMAC was tested on "all 13 maps",(Figure 6).
However, $ python -m smac.bin.map_list, would return 38 maps.
Could you please let me know, which 13 maps did you used for the experiments in the paper.
I get a error information when I try to run this code.
Traceback (most recent calls WITHOUT Sacred internals):
File "src/main.py", line 35, in my_main
run(_run, config, _log)
File "/home/liyurui/projects/MAIC/src/run.py", line 64, in run
run_sequential(args=args, logger=logger)
File "/home/liyurui/projects/MAIC/src/run.py", line 258, in run_sequential
learner.train(episode_sample, runner.t_env, episode)
File "/home/liyurui/projects/MAIC/src/learners/maic_learner.py", line 60, in train
mixer=self.target_mixer if self.supervised_loss_with_mixer else None,
AttributeError: 'MAICLearner' object has no attribute 'supervised_loss_with_mixer'
您好,请问您 maic_agent.py 代码文件中的 key 和 query 是不是弄反了?你把 trajectory 做成了 key,队友建模采样出来的 latent 作为 query,好像跟您原文不太一样?
Thank you for your wonderful work, and I really enjoyed reading it.
Just a quick question, could you please kindly provide a pointer to the superhard maps(MMM3, 3c_vs_100zg, etc.) that you used for the paper?
Thanks a lot
When I change the configuration in foraging.yaml like this:
env_args:
field_size: 20
players: 10
max_food: 6
force_coop: False
partially_observe: True
sight: 3
is_print: False
need_render: False
There will be an error report about gym.error.UnregisteredEnv.
So I wonder whether the environment configuration in foraging.yaml can be modified?
Looking foward to your reply.Thank you very much.
why in maic_agent.py, returns['entropy loss'] is not used in maic_learner?
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