ReinforcementLearning VS stable-baselines

Compare ReinforcementLearning vs stable-baselines and see what are their differences.

stable-baselines

Mirror of Stable-Baselines: a fork of OpenAI Baselines, implementations of reinforcement learning algorithms (by Stable-Baselines-Team)
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ReinforcementLearning stable-baselines
1 2
- 277
- 0.0%
- 0.0
- about 1 year ago
Python
- MIT License
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ReinforcementLearning

Posts with mentions or reviews of ReinforcementLearning. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-28.

stable-baselines

Posts with mentions or reviews of stable-baselines. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-28.

What are some alternatives?

When comparing ReinforcementLearning and stable-baselines you can also consider the following projects:

simglucose - A Type-1 Diabetes simulator implemented in Python for Reinforcement Learning purpose

SuperSuit - A collection of wrappers for Gymnasium and PettingZoo environments (being merged into gymnasium.wrappers and pettingzoo.wrappers

dueling-dqn-Reinforcement-learning - Line plot extraction using Reinforcement learning (Dueling DQN) using Keras

rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

Tic-Tac-Toe-Gym - This is the Tic-Tac-Toe game made with Python using the PyGame library and the Gym library to implement the AI with Reinforcement Learning

learning-to-drive-in-5-minutes - Implementation of reinforcement learning approach to make a car learn to drive smoothly in minutes

stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

aidoc - A simple CLI tool to generate documentation for your Python projects automatically.

voxelgym2D - Gymnasium based RL environment for 2D grid path planning

RLeXplore - RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).

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