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ray-project / ray

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[RLlib] Support Graph input batches

gjoliver
gjoliver commented Apr 13, 2022

Description

There are multiple user requests of using GraphNN data (node and edge lists) as sample batches into a custom RLlib model.

https://discuss.ray.io/t/rllib-variable-length-observation-spaces-without-padding/726
https://discuss.ray.io/t/working-with-graph-neural-networks-varying-state-space/5730/2

The recommended method today is to use Repeated observation space and VariableVal

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good first issue enhancement P2 rllib-models
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kmario23 / deep-learning-drizzle

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Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

machine-learning natural-language-processing deep-neural-networks reinforcement-learning computer-vision deep-learning optimization probability deep-reinforcement-learning medical-imaging speech-recognition artificial-neural-networks pattern-recognition probabilistic-graphical-models bayesian-statistics artificial-intelligence-algorithms visual-recognition geometric-deep-learning explainable-ai graph-neural-networks
  • Updated Apr 10, 2022
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annotated_deep_learning_paper_implementations

labmlai / annotated_deep_learning_paper_implementations

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🧑‍🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

machine-learning reinforcement-learning deep-learning transformers pytorch transformer gan neural-networks deep-learning-tutorial optimizers
  • Updated Apr 15, 2022
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stable-baselines

hill-a / stable-baselines

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Episode rewards not updated before being used by callback.on_step()

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calerc
calerc commented Nov 23, 2020

The following applies to DDPG and TD3, and possibly other models. The following libraries were installed in a virtual environment:

numpy==1.16.4
stable-baselines==2.10.0
gym==0.14.0
tensorflow==1.14.0

Episode rewards do not seem to be updated in model.learn() before callback.on_step(). Depending on which callback.locals variable is used, this means that:

  • episode rewards may n
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good first issue question

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