Comments (4)
Hi, thanks for your interesting, the codes are already open-sourced. Please check the readme. MASAC is not the algorithm we proposed in the paper
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Thank you for your response!
I reviewed the README file and noticed that you have open-sourced four algorithms: MAA2C, MAPPO, MAACKTR, and MADQN. Additionally, I saw that in the Training Curves section of the README, MAA2C, MAPPO, and MAACKTR are the baseline algorithms you used for comparison. I recall that you mentioned in your paper that your proposed algorithm is an improved version based on A2C.
Therefore,I have three questions I hope you can help me with:
-
Are your baseline algorithms the ones mentioned above (without parameter sharing, action masking, local reward design, and a priority-based safety supervisor), and is the "ours" algorithm the MAA2C with the new features added? (Referring to the MAA2C source code in the link: https://drive.google.com/drive/folders/1CPOOYSQzqc0_XUr8durNWDUBv66PUHaM, as mentioned in issue #27)
-
Are the new features toggled in the following sections?
- Safety supervisor: config.[ENV_CONFIG].safety_guarantee
- Regional reward: config.[MODEL_CONFIG].reward_type
- Action masking: config.[MODEL_CONFIG].action_masking
- Parameter sharing: not found -
Where should parameter sharing be adjusted (if it can be adjusted)?
Thank you for your response, and best of luck with your research!
from marl_cavs.
Thank you for your response!
I reviewed the README file and noticed that you have open-sourced four algorithms: MAA2C, MAPPO, MAACKTR, and MADQN. Additionally, I saw that in the Training Curves section of the README, MAA2C, MAPPO, and MAACKTR are the baseline algorithms you used for comparison. I recall that you mentioned in your paper that your proposed algorithm is an improved version based on A2C.
Therefore,I have three questions I hope you can help me with:
- Are your baseline algorithms the ones mentioned above (without parameter sharing, action masking, local reward design, and a priority-based safety supervisor), and is the "ours" algorithm the MAA2C with the new features added? (Referring to the MAA2C source code in the link: https://drive.google.com/drive/folders/1CPOOYSQzqc0_XUr8durNWDUBv66PUHaM, as mentioned in issue About the baseline in the paper #27)
- Are the new features toggled in the following sections?
- Safety supervisor: config.[ENV_CONFIG].safety_guarantee
- Regional reward: config.[MODEL_CONFIG].reward_type
- Action masking: config.[MODEL_CONFIG].action_masking
- Parameter sharing: not found
- Where should parameter sharing be adjusted (if it can be adjusted)?
Thank you for your response, and best of luck with your research!
- Yes, the google drive link contains the main codes with all features.
- Yes, all the features can be tuned in the configure file
- If parameter sharing means the parameter sharing among agents, that is included in the network, you may find it in the model files. If you mean parameter sharing between actor and critic networks, you can set `shared_network = True'
Thanks
from marl_cavs.
Thanks for your reply!
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Related Issues (20)
- About the baseline in the paper HOT 8
- Environment problem HOT 1
- help!! HOT 1
- error HOT 6
- How do I get information about the speed of the vehicles around me? HOT 11
- How to modify to achieve multi lane scenario? HOT 1
- TypeError: RandomNumberGenerator._generator_ctor() takes from 0 to 1 positional arguments but 2 were given HOT 9
- About baseline in the paper? HOT 4
- A question about curriculum learning HOT 4
- Problem running mappo, obs_state is a 5*5 mat, but the actor model's imput is set as 25 HOT 4
- MASAC algorithm HOT 1
- ERROR:root:Can not find checkpoint for ./results/Apr_19_08_30_17/models/
- about SubprocVecEnv HOT 2
- Error: The ‘eval_rewards.npy’ file could not be found while drawing comparison curves HOT 1
- Regarding the running errors of run_madqn HOT 2
- Training data and evaluation data HOT 10
- Simulation and Emulation HOT 2
- Program repetition training and exploration
- About MAPPO with Safety Supervisor and Action Mask HOT 3
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