multiagent-particle-envs
transferlearning
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| multiagent-particle-envs | transferlearning | |
|---|---|---|
| 6 | 1 | |
| 2,188 | 12,841 | |
| 3.6% | - | |
| 0.0 | 7.6 | |
| 19 days ago | 9 days ago | |
| Python | Python | |
| MIT License | MIT License |
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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
transferlearning
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[D] Medium Article: Adaptive Learning for Time Series Forecasting
The src is available in https://github.com/jindongwang/transferlearning I'll also publish about how to code the model for time series
What are some alternatives?
rpg_timelens - Repository relating to the CVPR21 paper TimeLens: Event-based Video Frame Interpolation
zshot - Zero and Few shot named entity & relationships recognition
maddpg - Code for the MADDPG algorithm from the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
stackoverflow-better-stats - Better statistics about Stack Overflow's 2023 Developer Survey
ALAE - [CVPR2020] Adversarial Latent Autoencoders
PaddleHelix - Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集
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.
awesome-artificial-intelligence-research - A curated list of Artificial Intelligence (AI) Research, tracks the cutting edge trending of AI research, including recommender systems, computer vision, machine learning, etc.
TS-TCC - [IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
Transfer-Learning-Library - Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
Efficient-VDVAE - Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"
FSL-Mate - FSL-Mate: A collection of resources for few-shot learning (FSL).