maddpg
ChatPaper
maddpg | ChatPaper | |
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2 | 2 | |
1,540 | 17,765 | |
2.5% | - | |
0.0 | 7.3 | |
about 2 months ago | about 2 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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maddpg
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How is the backward pass performed in MADDPG algorithm from MARL
I'm using the MADDPG algorithm from https://github.com/openai/maddpg/blob/master/maddpg/trainer/maddpg.py. I understood the forward pass for both the actor and critic networks. I'm not able to understand how the actor and critic networks are updates. Like at line 188 and 191 the authors compute the critic loss and actor loss. But can anyone explain how the critic and actor networks are updated. Also, as far as I understand, when the number of agents increases from 3 to 6 for a simple spread policy in MADDPG, the computation time for Q loss and P loss at lines 188 and 191 increase super-linearly. I'm assuming this might be because both the Q loss and P loss utilize the Q values and the dimension to calculate the Q values increases with the number of increasing linearly. It would be great if anyone can help me to understand this back propagation phase much better and why does the computation time grow super-linearly. I also put a time counter to track the computation time of Q loss and P loss for 60,000 episodes with simple spread policy (3 agents, 3 landmarks, 0 adversaries). Thanks for the help, in advance! **Q loss** 3 agents 74.31 sec 6 agents 243.31 sec (3X) **P loss** 3 agents 114.86 sec 6 agents 321.76 sec (3x)
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How to get my multi-agents more collaborative?
Another thing is that I don't use only one centralized critic, I'm using one for each agent (they are all centralized), you could use parameter sharing for the ones of the same type if you want. A great start would be to look at how the MADDPG works in an implementation (original, tf2 ,pytorch-1 , pytorch-2 ), then you can see how it is the training of the actor and the critic and just adapt the ideas to your MA-PPO implementation.
ChatPaper
- GitHub - kaixindelele/ChatPaper: Use ChatGPT to summarize the arXiv...GitHub - kaixindelele/ChatPaper: Use ChatGPT to summarize the arXiv...
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[R] OpenChatPaper - Yet another paper reading assistant based on ChatGPT
An open-source version that attempts to reimplement ChatPDF. A different dialogue version of another ChatPaper project.
What are some alternatives?
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
OpenChatPaper - Yet another paper reading assistant based on OpenAI ChatGPT API. An open-source version that attempts to reimplement ChatPDF. A different dialogue version of another ChatPaper project.
pymarl - Python Multi-Agent Reinforcement Learning framework
multiagent-particle-envs - Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"
transferlearning - Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习