rl-baselines3-zoo
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rl-baselines3-zoo | gym | |
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11 | 96 | |
1,777 | 33,846 | |
5.0% | 0.8% | |
6.3 | 0.0 | |
24 days ago | 18 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
gym
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OpenAI Acquires Global Illumination
A co-founder announced they disbanded their robots team a couple years ago: https://venturebeat.com/business/openai-disbands-its-robotic...
That was the same time they depreciated OpenAI Gym: https://github.com/openai/gym
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:
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Some confusion about variables and functions in mujoco-py
When I browse fetch_env.py, I have a question about the following code snippet:
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pip install stable-baselines3[extra]
Nvm, this works for me '!pip install setuptools==65.5.0' Source: https://github.com/openai/gym/issues/3176
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[P] Reinforcement learning evolutionary hyperparameter optimization - 10x speed up
how would this interact/compare with https://github.com/openai/gym?
- What has replaced OpenAI Retro Gym?
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Understanding Reinforcement Learning
If you'd like to learn more about reinforcement learning or play with a number of samples in controlled environments, I highly recommend you look at the documentation for OpenAI's Gym library and particularly the basic usage page. OpenAI's Gym provides a standardized environment for performing reinforcement learning on classic Atari games and a few other platforms and should be an educational resource. If you'd like a more detailed example, check out this tutorial on Paperspace's blog.
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Using the cross-entropy method to solve Frozen Lake
Frozen Lake is an OpenAI Gym environment in which an agent is rewarded for traversing a frozen surface from a start position to a goal position without falling through any perilous holes in the ice.
- Is there a publicly available state space model for the Lunar Lander environment?
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How to Create a Behavioral Cloning Bot to Play Online Games?
typically a more relaxed approach is taken via reinforcement learning, but it requires that you can simulate the game via a given gamestate. take a look at e.g. https://www.gymlibrary.dev/
What are some alternatives?
optuna - A hyperparameter optimization framework
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
carla - Open-source simulator for autonomous driving research.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
tensorflow - An Open Source Machine Learning Framework for Everyone
gym-pybullet-drones - PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
rl-baselines-zoo - A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
pybullet-gym - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.
rlcard - Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.