learning-to-drive-in-5-minutes
rl-baselines3-zoo
| learning-to-drive-in-5-minutes | rl-baselines3-zoo | |
|---|---|---|
| 1 | 11 | |
| 277 | 1,791 | |
| - | 3.1% | |
| 0.0 | 6.2 | |
| about 2 years ago | 4 days ago | |
| Python | Python | |
| MIT License | MIT License |
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learning-to-drive-in-5-minutes
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TIL that in 1986 an astronomer trying to trace a 75 cent computer time discrepancy for 10 months eventually found a German hacker selling defense secrets to the KGB
You might already be using it, but if not I'd recommend checking out /u/araffin2 's GitHub repo https://github.com/araffin/learning-to-drive-in-5-minutes
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
What are some alternatives?
stable-baselines3-contrib - Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code
optuna - A hyperparameter optimization framework
autonomous-learning-library - A PyTorch library for building deep reinforcement learning agents.
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
stable-baselines - Mirror of Stable-Baselines: a fork of OpenAI Baselines, implementations of reinforcement learning algorithms
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
rl-trained-agents - A collection of pre-trained RL agents using Stable Baselines3
gym-pybullet-drones - PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
rl-baselines-zoo - A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
pybullet-gym - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.
gym - A toolkit for developing and comparing reinforcement learning algorithms.