Comments (5)
I notice that in your code the multiagent mujoco environment is an MDP setting. Thus, the inputs of critics of IPPO and MAPPO are the same. I expect the performances to be similar but the results in the figure are not. Are there other factors I'm ignoring? I am looking forward to your reply. Thank you!
Hi, I have the same confusion, have you figured it out?
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I notice that in your code the multiagent mujoco environment is an MDP setting. Thus, the inputs of critics of IPPO and MAPPO are the same. I expect the performances to be similar but the results in the figure are not. Are there other factors I'm ignoring? I am looking forward to your reply. Thank you!
Hi, I have the same confusion, have you figured it out?
Not yet. (ToT) Waiting for the author's reply.
from trpo-in-marl.
I notice that in your code the multiagent mujoco environment is an MDP setting. Thus, the inputs of critics of IPPO and MAPPO are the same. I expect the performances to be similar but the results in the figure are not. Are there other factors I'm ignoring? I am looking forward to your reply. Thank you!
Hi, I have the same confusion, have you figured it out?
Not yet. (ToT) Waiting for the author's reply.
Our IPPO baseline following the origin paper setting, i.e. independent learning (not CTDE), and POMDP.
If you want to reproduce this result, for not CTDE setting, you can set use_centralized_V as False.
If you want modify input of policy as local observation, you can modify the function get_obs() in mujoco_multi.py line 156, uncomment line 156 and comment line 157. Hope this can help you!
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Thanks very much for your reply!
However, I'm still confused.
For multi-agent mujoco, the observation is the same as the state ##11, then use_centralized_V makes no difference.
Then I want to confirm if the difference between IPPO and MAPPO in your code is just the setting of use_centralized_V as https://github.com/marlbenchmark/on-policy?
If this is true, could I expect the performances to be similar?
Looking forward to your reply~
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Thanks very much for your reply! However, I'm still confused. For multi-agent mujoco, the observation is the same as the state ##11, then use_centralized_V makes no difference. Then I want to confirm if the difference between IPPO and MAPPO in your code is just the setting of use_centralized_V as https://github.com/marlbenchmark/on-policy? If this is true, could I expect the performances to be similar? Looking forward to your reply~
No, to follow the origin paper, in IPPO we use POMDP setting, if you want to set this, you can modify the function get_obs() in mujoco_multi.py line 156, uncomment line 156 and comment line 157. Hope this can help you!
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Related Issues (19)
- About the number of Critic Networks HOT 3
- How do you use global information and local information in multi-agent mujoco? HOT 1
- I found that the action value exceeds the limit HOT 1
- muti_env_error HOT 4
- gym error
- The question about critic loss
- I found a bug in file 'utils/util.py'. If we use discrete action space in 'runners\separated\mujoco_runner.py' and store it's transition in buffer, we will get a bug. Because the act_shape is a constant value. HOT 2
- I have some questions about the adjustment of experiment parameters. HOT 3
- conflicting dependicies and distribution of some packages not found HOT 1
- Some questions about HAPPO implementation HOT 2
- Question about observation and state in multi-agent mujoco tasks HOT 1
- Do you have PyMARL implementation? HOT 1
- The
- The Script code runs wrong when applying the HATRPO algorithm with 【rnn】 network. HOT 1
- what to do with a dead agent HOT 1
- Question about HAPPO performance in StarCraftII
- Questions about visualization
- dependency issue HOT 1
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