cleanrl
reinforcement-learning-discord-wiki
cleanrl | reinforcement-learning-discord-wiki | |
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41 | 10 | |
4,493 | 233 | |
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6.3 | 1.8 | |
11 days ago | over 3 years ago | |
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GNU General Public License v3.0 or later | - |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cleanrl
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[P] PettingZoo 1.24.0 has been released (including Stable-Baselines3 tutorials)
PettingZoo 1.24.0 is now live! This release includes Python 3.11 support, updated Chess and Hanabi environment versions, and many bugfixes, documentation updates and testing expansions. We are also very excited to announce 3 tutorials using Stable-Baselines3, and a full training script using CleanRL with TensorBoard and WandB.
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PPO agent for "2048": help requested
Here's where the problem starts: after implementing a custom environment that follows the typical gymnasium interface, and use a slightly adjusted PPO implementation from CleanRL, I cannot get the agent to learn anything at all, even though this specific implementation seems to work just fine on basic gymnasium examples. I am hoping the RL community here can help me with some useful pointers.
- [P] 10x faster reinforcement learning hyperparameter optimization than SOTA - now with distributed training!
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PPO ignores high rewards in deterministic sytem
Try out a standard implementation with some standard parameters from here: https://github.com/vwxyzjn/cleanrl/tree/master/cleanrl
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SB3 - NotImplementedError: Box([-1. -1. -8.], [1. 1. 8.], (3,), <class 'numpy.float32'>) observation space is not supported
I am trying to run cleanrl on the `Pendulum-v1` environment. I did that by going here and changing the default `env-id` to ` parser.add_argument("--env-id", type=str, default="Pendulum-v1",
- Cartpole and mountain car
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cleanrl gym issues
git clone https://github.com/vwxyzjn/cleanrl.git && cd cleanrl poetry install
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Why is my Soft Actor Critic Algorithm not learning?
Can someone please help me debug my implementation of SAC. Please let me know if you have any questions. I tried comparing my work with CleanRL and caught a couple of errors. However, my implementation does diverge a lot from theirs as I wanted to test my understanding.
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Model-based hierarchical reinforcement learning
Shameless self-plug: as far as implementation is concerned, I am working on a (hopefully) easier to understand Dreamer architecture under the CleanRL library, toward also re-implementing Director, Dreamer-v3, and and JAX variant for faster training.
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[P] Robust Policy Optimization is now in CleanRL š„!
Happy to share that CleanRL now has a new algorithm called Robust Policy Optimization ā 5 lines of code change to PPO to get better performance in 57 out of 61 continuous action envs š (e.g., dm_control)
reinforcement-learning-discord-wiki
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Iām 15 and this is yeet
There are a few good subreddits that I could recommend. If you want to go in the direction of RL then /r/reinforcementlearning is the one and I would highly suggest that you join the Discord server as well. People are really friendly there and provide a lot of help if you have questions.
- I want to learn but don't know where to start.
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Tips for a DRL PhD student
This discord is pretty good https://github.com/andyljones/reinforcement-learning-discord-wiki/wiki
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How do I go beyond just using the framework implementation of RL algorithms?
This is also a good resource if you are facing other problems.
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I want a study buddy for studying deep reinforcement learning
We've got an active Discord here. Isn't explicitly a study group, but has plenty of other newbies asking questions.
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Mastering Real-Time Strategy Games with Deep RL: Mere Mortal Edition
Thank you for the kind words! I am also quite excited about the new points in game design space that RL will unlock and am planning write another blogpost on that topic.
I quite like https://karpathy.github.io/2016/05/31/rl/ as an introduction to some of the ideas behind modern RL. Beyond that, I just recently found out about https://github.com/andyljones/reinforcement-learning-discord... which lists a lot of other high-quality resources.
CodeCraft is a programming which you can "play" by writing a Scala/Java program that controls the game units. It's not actively developed anymore but still functional: http://codecraftgame.org/
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[D] Debugging Reinforcement Learning Systems Without the Agonizing Pain
This article is a collection of debugging advice that has served me well over the past few years. It's collated both from my personal experiences, and from several months of discussion in the RL Discord. It is intended as compliment to the other excellent advice that can be found elsewhere.
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RL Discord Talk: Neural MMO: A massively multiagent environment
Every Saturday on the RL Discord we host a talk in the voice channel by a server regular. The talk is typically 10-30 mins long with questions and open discussion afterwards.
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A good RL course among these?
We've collated some recommendations on the RL Discord wiki.
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Question: What's a good source to start learning RL?
There's a selection of popular resources on the RL Discord wiki.
What are some alternatives?
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
pybullet-gym - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.
tianshou - An elegant PyTorch deep reinforcement learning library.
d3rlpy - An offline deep reinforcement learning library
mbrl-lib - Library for Model Based RL
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
sample-factory - High throughput synchronous and asynchronous reinforcement learning
wandb - š„ A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
Deep-Reinforcement-Learning-Algorithms-with-PyTorch - PyTorch implementations of deep reinforcement learning algorithms and environments
spinningup - An educational resource to help anyone learn deep reinforcement learning.
deep_rl_zoo - A collection of Deep Reinforcement Learning algorithms implemented with PyTorch to solve Atari games and classic control tasks like CartPole, LunarLander, and MountainCar.
dm_env - A Python interface for reinforcement learning environments