on-policy
pymarl2
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| on-policy | pymarl2 | |
|---|---|---|
| 12 | 1 | |
| 1,125 | 556 | |
| 7.8% | - | |
| 4.9 | 5.0 | |
| 10 days ago | 4 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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on-policy
- How do you compute rewards when you are using parallel environments?
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Renderer of the environment does not work?
I am trying to feed the agents with visual observation and thus using the renderer of this environment (https://github.com/marlbenchmark/on-policy/blob/main/onpolicy/envs/mpe/rendering.py), but I get this as an image:
- Stuck on this error for days: I can't use importlib the right way
- Difference between setup.py, environments.yaml and requirements.txt
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Ubuntu terminal crashes when I launch a deep reinforcement learning model
I am trying to run this code on my Ubuntu machine (https://github.com/marlbenchmark/on-policy).
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"chmod" is not recognized as an internal or external command, operable program or batch file
If you don't want to install a Linux VM, the other option is to read the source of the train_mpe.sh script and write your own version as a Windows batch file.
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Confused between "centralized critic" and "centralized training decentralized execution"
Sorry, this was the paper: https://arxiv.org/abs/2104.07750 But I guess you already answered my question. Indeed, agents receive a global obervation, but cannot directly observe other agents' actions, states, orrewards, and do not share parameters. So if I understand correctly that what they're using here is independent PPO with global observation, but no centralized critic. Which is what MAPPO (https://github.com/marlbenchmark/on-policy/blob/main/onpolicy/algorithms/r_mappo/algorithm/r_actor_critic.py) does: centralized observation space, but (if I'm correct), decentralized critic.
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Why is this implementation of PPO using a replay buffer?
I don't see the buffer being cleared anywhere, but it looks to me like it may not need to... For example, the implementation of SeparatedReplayBuffer receives the episode_length (or "horizon" as is sometimes called) and sets the size of the buffer accordingly when its initialized. That way, the amount of samples collected before each policy/value update is constant. You just need one giant tensor block to collect all your samples, then after doing a networks update, why clear them out? Just overwrite the existing samples, since you know you'll collect exactly the same number of new samples.
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MARL top conference papers are ridiculous
https://github.com/marlbenchmark/on-policy (MAPPO-FP)
pymarl2
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MARL top conference papers are ridiculous
https://github.com/hijkzzz/pymarl2 (RIIT)
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