multiagent-particle-envs
gpt-2
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| multiagent-particle-envs | gpt-2 | |
|---|---|---|
| 6 | 63 | |
| 2,188 | 21,111 | |
| 3.6% | 1.9% | |
| 0.0 | 2.5 | |
| 19 days ago | 20 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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multiagent-particle-envs
- Why is Q-learning always presented in such a math-heavy fashion? I just spent an hour dissecting this formula with a student -- only to strongly suspect there is a typo. Are there any good Q-Learning tutorials out there that *explain* the math instead of dropping it from the sky?
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Ideal size of the visual observation
Hi, I am using the MPE (https://github.com/openai/multiagent-particle-envs) and I'm planning to use a visual observation. I was wondering, what size should it be? I assume that if it is too large and the agents are only a few, I am wasting lots of compute for nothing and also the noise becomes a lot. But how to find the best size? 60x60x3 for example?
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Why does MADDPG use action log prob for Q (Critic) instead of sampled action?
Code for https://arxiv.org/abs/1706.02275 found: https://github.com/openai/multiagent-particle-envs
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I get an incredibly long error simply for trying to install an older version of Numpy
git clone https://github.com/openai/multiagent-particle-envs cd multiagent-particle-envs/ python -m venv maddpg echo env/ >> .gitignore .\maddpg\Scripts\activate pip install gym==0.10.5 pip install numpy==1.14.5
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Not as impressive as the one natural simulator that I made with visual programming languages Aka the future but this is good try for someone learning programming
If you wanted to make it an RL env that others could train, it might be a nicer looking version of https://github.com/openai/multiagent-particle-envs
gpt-2
- 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.
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Elon Musk, Y Bengio, Andrew Yang etc called for a temporary pause on training systems exceeding GPT-4
Elon's 100M put this in the arena. https://github.com/openai/gpt-2
What are some alternatives?
rpg_timelens - Repository relating to the CVPR21 paper TimeLens: Event-based Video Frame Interpolation
dalle-mini - DALL·E Mini - Generate images from a text prompt
maddpg - Code for the MADDPG algorithm from the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
minGPT - A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
ALAE - [CVPR2020] Adversarial Latent Autoencoders
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
qlib - Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
transferlearning - Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
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