rl-baselines3-zoo
sbx
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rl-baselines3-zoo | sbx | |
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11 | 5 | |
1,786 | 262 | |
5.0% | - | |
6.2 | 5.9 | |
about 5 hours ago | 17 days ago | |
Python | Python | |
MIT License | MIT License |
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rl-baselines3-zoo
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Can't solve MountainCar-v0 with A2C algorithm (stable-baselines3)
I'm trying to solve MountainCar-v0 enviroment from gymnasium with the A2C algorithm and the agent doesn't find a solution. I checked this so I added import stable_baselines3.common.sb2_compat.rmsprop_tf_like as RMSpropTFLike. Also checked the rl-baselines3-zoo for the hyperparameter tuning. So my code is:
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Stable-Baselines3 v2.0: Gymnasium Support
RL Zoo3 (training framework): https://github.com/DLR-RM/rl-baselines3-zoo
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Tips and Tricks for RL from Experimental Data using Stable Baselines3 Zoo
I'm still new to the domain but wanted to shared some experimental data I've gathered from massive amount of experimentation. I don't have a strong understanding of the theory as I'm more of a software engineer than data scientist, but perhaps this will help other implementers. These notes are based on Stable Baselines 3 and RL Baselines3 Zoo with using PPO+LSTM (should apply generally to all the algos for the most part)
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Simple continuous environment with spaceship but yet challenging for RL algorithms (like SAC, TD3)
Try hyperparameter search. It's implemented here: https://github.com/DLR-RM/rl-baselines3-zoo for stable-baselines3. Hyperparameters make a huge difference in RL, much more than in supervised learning.
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Easily load and upload Stable-baselines3 models from the Hugging Face Hub 🤗
Integrating RL-baselines3-zoo
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Help comparing Double DQN against another paper's results
Hello, I've been running some tests of Double DQN with Stable Baselines 3 Zoo and to compare I'm using the graphs provided by Noisy Networks For Exploration.
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DDPG not solving MountainCarContinuous
- you can find tuned hyperparameters for DDPG, SAC, PPO in https://github.com/DLR-RM/rl-baselines3-zoo
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Hyperparameter tuning examples
For more complete implementation: https://github.com/DLR-RM/rl-baselines3-zoo
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How do I convert zoo / gym trained models to TensorFlow Lite or PyTorch TorchScript?
https://github.com/DLR-RM/rl-baselines3-zoo (PyTorch based, using https://github.com/DLR-RM/stable-baselines3)
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[P] Stable-Baselines3 v1.0 - Reliable implementations of RL algorithms
We also release 100+ trained models in our experimental framework, the rl zoo: https://github.com/DLR-RM/rl-baselines3-zoo
sbx
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Stable-Baselines3 v2.0: Gymnasium Support
Stable-Baselines Jax (SBX): https://github.com/araffin/sbx
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JAX in Reinforcement Learning
If you want to learn from examples, you can take a look at clean rl or stable baselines jax (sbx): https://github.com/araffin/sbx
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How can I speed up SAC?
You mean wallclock time or sample efficiency? For the former, you can take a look at Jax implementation like: https://github.com/araffin/sbx (SB3 + Jax)
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Stable-Baselines3 v1.8 Release
The Hindsight Experience Replay (HER) buffer is compatible with all off-policy reinforcement learning algorithms. (and also compatible with the Jax version of SB3: https://github.com/araffin/sbx/pull/11).
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JAX or PyTorch?
I haven't used JAX yet but quite excited about it - Little plug for SBX https://github.com/araffin/sbx which seems quite clean!
What are some alternatives?
optuna - A hyperparameter optimization framework
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
jaxrl - JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.
jaxrl_m - Skeleton for scalable and flexible Jax RL implementations
gym-pybullet-drones - PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
fselect - Find files with SQL-like queries
rl-baselines-zoo - A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
Study-Time-Tally - Track your study hours.
pybullet-gym - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.
Terminal-Video-Player - A program that can display video in the terminal using ascii characters