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JORLDY: OpenSource Reinforcement Learning Framework
2 projects | reddit.com/r/reinforcementlearning | 8 Nov 2021
Distributed RL algorithms are provided using ray
Python stands to lose its GIL, and gain a lot of speed
5 projects | reddit.com/r/programming | 20 Oct 2021
I had a similar use case and ended up using ray. https://github.com/ray-project/ray
How to deploy a rllib-trained model?
3 projects | reddit.com/r/reinforcementlearning | 16 Oct 2021
Currently, rllib's "--export-formats" does nothing; I have folders of checkpoints, but no models. Looks like currently the internal export_model function isn't implemented: https://github.com/ray-project/ray/issues/190213 projects | reddit.com/r/reinforcementlearning | 16 Oct 2021
[HELP] Converting many individual workstations into a HPC cluster
1 project | reddit.com/r/HPC | 11 Oct 2021
Unless you have infiniband, you might want to build it as a kubernetes cluster and look at something like (ray-project)[https://github.com/ray-project/ray] it has a ton of distributed plugin packages that are Ethernet based.
Show HN: SpotML – Managed ML Training on Cheap AWS/GCP Spot Instances
6 projects | news.ycombinator.com | 3 Oct 2021
Neat. Congratulations on the launch!
Apart from the fact that it could deploy to both GCP and AWS, what does it do differently than AWS Batch ?
When we had a similar problem, we ran jobs on spots with AWS Batch and it worked nicely enough.
Some suggestions (for a later date):
1. Add built-in support for Ray  (you'd essentially be then competing with Anyscale, which is a VC funded startup, just to contrast it with another comment on this thread) and dbt .
2. Support deploying coin miners (might be good to widen the product's reach; and stand it up against the likes of consensys).
3. Get in front of many cost optimisation consultants out there, like the Duckbill Group.
If I may, where are you building this product from? And how many are on the team?
Writing your First Distributed Python Application with Ray (without multiprocessing)
4 projects | reddit.com/r/Python | 23 Aug 2021
Here is an older discussion on dask vs ray from the creators of both projects: https://github.com/ray-project/ray/issues/642
[D] Kubeflow vs. Argo for ML Pipeline Tool
2 projects | reddit.com/r/MachineLearning | 17 Aug 2021
Here is link number 1 - Previous text "Ray"2 projects | reddit.com/r/MachineLearning | 17 Aug 2021
If you are looking for a developer-friendly tool, I'd ditch the task/workflow orchestration paradigm altogether and use something like Ray. It's made by and for ML practitioners, it's much more versatile, has no unwarranted DSLs (pure python), and you can test locally before deploying with pretty much the same code.
1 project | news.ycombinator.com | 8 Jun 2021
Fully trained model for Atari Pong?
1 project | reddit.com/r/reinforcementlearning | 5 Dec 2021
Check out this library: https://github.com/DLR-RM/stable-baselines3. Maybe you can find the pre-trained agent you are looking for.
Can I separate out the steps of learn() in stable baselines3?
1 project | reddit.com/r/reinforcementlearning | 26 Nov 2021
How do I train my custom made quadcopter using reinforcement learning using ROS, Gazebo and Openai_ros?
1 project | reddit.com/r/ROS | 31 Oct 2021
to verify that all the gym environment is working well.stable-baselines3, which has a method to verify that all the gym environment is working well.
[P] Introducing Godot RL Agents
2 projects | reddit.com/r/MachineLearning | 15 Oct 2021
The library has a standard gym wrapper. Supports training of RL agents with Ray rllib and StableBaselines3.
[D] Tired of writing mundane data wrangling code.
1 project | reddit.com/r/MachineLearning | 13 Oct 2021
You can always do better. Whenever I get comfortable with a certain framework/project structure, I tend to shoo away other approaches. For example, when I was just beginning I would never use argument parsers in my scripts, now I can not work without them. The way I learn new techniques is generally through exploring other people's repositories. I work a lot in Reinforcement Learning, so I am constantly looking at other people's code. Take stabelbaselines, I have pondered through their library many times looking for things I need, and in the process I have found new techniques to define my functions, organize my models, and so on.
A PyTorch RL and DL framework I have built
2 projects | reddit.com/r/reinforcementlearning | 12 Oct 2021
It looks pretty interesting, having an adapter to use stable-baselines3 would be useful as well just to have an easy way of integrating those, well, baselines, into the project for comparing the algorithm under development.
I need suggestions to improve my project
3 projects | reddit.com/r/github | 6 Sep 2021
Hello everyone, I published my python project a month ago, it's a command line interface for training, tuning and reusing reinforcement learning algorithms in tensorflow 2.x. It's similar to stable-baselines, tf-agents, and not so many others. It seems like it's not getting enough attention despite the README, license, and everything else.
Stable-Baselines3 v1.1.0: Dictionary observation support, timeout handling and refactored HER buffer
1 project | reddit.com/r/reinforcementlearning | 2 Jul 2021
It brings one of our most requested feature: dictionary observation (mixed obs) support (https://github.com/DLR-RM/stable-baselines3/issues/216).
1 project | reddit.com/r/reinforcementlearning | 25 Jun 2021
If you look e.g. at the algorithms implemented in the stablebaseline3 RL library, https://github.com/DLR-RM/stable-baselines3 in the section "Implemented Algorithms" you'll see in the "Box" column (your 8 actions can be seen as a point in an 8-dimensional box) that DQN is the only algorithm there that *doesn't* work with continuous action space.
[Question] About torch distribution, TanhDistribution: Is this a pyTorch bug or am I doing it wrong?
2 projects | reddit.com/r/reinforcementlearning | 12 May 2021
you will probably find an answer in this discussion: https://github.com/DLR-RM/stable-baselines3/issues/207
What are some alternatives?
Faust - Python Stream Processing
gevent - Coroutine-based concurrency library for Python
SCOOP (Scalable COncurrent Operations in Python) - SCOOP (Scalable COncurrent Operations in Python)
Thespian Actor Library - Python Actor concurrency library
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
tianshou - An elegant PyTorch deep reinforcement learning library.
optuna - A hyperparameter optimization framework
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, Ape-X DQN, DDPG, TD3, SAC)