maddpg
gpt-2
maddpg | gpt-2 | |
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2 | 64 | |
1,524 | 21,146 | |
1.8% | 1.1% | |
0.0 | 2.5 | |
about 1 month ago | 25 days 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.
gpt-2
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What are LLMs? An intro into AI, models, tokens, parameters, weights, quantization and more
Medium models: Roughly between 1B to 10B parameters. This is where Mistral 7B, Phi-3, Gemma from Google DeepMind, and wizardlm2 sit. Fun fact: GPT 2 was a medium sized model, much smaller than its latest versions.
- Sam Altman is still trying to return as OpenAI CEO
- Build Personal ChatGPT Using Your Data
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Are the recent advancements in AI technology primarily driven by recent discoveries or the progress in hardware capabilities and the abundance of available data?
"Our model, called GPT-2 (a successor to GPT), was trained simply to predict the next word in 40GB of Internet text. Due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper. "
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BING IS NOW THE DEFAULT SEARCH FOR CHATGPT
They did release GPT-2 under the MIT License.
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Don Knuth Plays with ChatGPT
Did you arrive at this certainty through reading something other than what OpenAI has published? The document [0] that describes the training data for GPT-2 makes this assertion hilarious to me.
[0]: https://github.com/openai/gpt-2/blob/master/model_card.md#da...
- Was frustriert euch an der Nutzung oder der Diskussion um KI?
- The AI
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Help with pet project to learn - Running ChatGPT-2 at home
I made a clone of https://github.com/openai/gpt-2 on my local laptop
- По поводу опасности ИИ и предложений остановить разработки на 6 месяцев.
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.
dalle-mini - DALL·E Mini - Generate images from a text prompt
pymarl - Python Multi-Agent Reinforcement Learning framework
minGPT - A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
multiagent-particle-envs - Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
transferlearning - Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
sentencepiece - Unsupervised text tokenizer for Neural Network-based text generation.
jukebox - Code for the paper "Jukebox: A Generative Model for Music"
mesh-transformer-jax - Model parallel transformers in JAX and Haiku
gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.