jaxrl
cleanrl
| jaxrl | cleanrl | |
|---|---|---|
| 2 | 41 | |
| 576 | 4,493 | |
| - | - | |
| 0.0 | 6.3 | |
| over 1 year ago | 10 days ago | |
| Jupyter Notebook | Python | |
| MIT License | GNU General Public License v3.0 or later |
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jaxrl
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JAX in Reinforcement Learning
Have you looked at this repo or this repo ?
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CleanRL now has a DDPG + JAX implementation roughly 2.5-4x faster than DDPG + PyTorch
https://github.com/ikostrikov/jaxrl would be another great reference implementation. Probably you want to also checkout the docs for jax, flax, and optax.
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 🔥!
Happy to share that CleanRL now has a new algorithm called Robust Policy Optimization — 5 lines of code change to PPO to get better performance in 57 out of 61 continuous action envs 🚀 (e.g., dm_control)
What are some alternatives?
jaxrl_m - Skeleton for scalable and flexible Jax RL implementations
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
sbx - SBX: Stable Baselines Jax (SB3 + Jax)
tianshou - An elegant PyTorch deep reinforcement learning library.
indaba-pracs-2022 - Notebooks for the Practicals at the Deep Learning Indaba 2022.
d3rlpy - An offline deep reinforcement learning library
Popular-RL-Algorithms - PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
reinforcement-learning-discord-wiki - The RL discord wiki
uvadlc_notebooks - Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
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