Open Source Linux Reinforcement Learning Frameworks - Page 4
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Reinforcement Learning Frameworks for Linux
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PaLM + RLHF - Pytorch
Implementation of RLHF (Reinforcement Learning with Human Feedback)
PaLM-rlhf-pytorch is a PyTorch implementation of Pathways Language Model (PaLM) with Reinforcement Learning from Human Feedback (RLHF). It is designed for fine-tuning large-scale language models with human preference alignment, similar to OpenAI’s approach for training models like ChatGPT. -
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Parallel Reinforcement Evolutionary Artificial Neural Networks (PREANN) is a framework of flexible multi-layer ANN's with reinforcement learning based on genetic algorithms and a parallel implementation (using XMM registers and NVIDIA's CUDA).
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PettingZoo
An API standard for multi-agent reinforcement learning environments
PettingZoo is a standardized API and library for multi-agent reinforcement learning (MARL) environments. It provides a broad set of environments and tools to facilitate the development and evaluation of multi-agent algorithms. -
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Physical Symbolic Optimization (Φ-SO)
Physical Symbolic Optimization
Physical Symbolic Optimization (Φ-SO) - A symbolic optimization package built for physics. Symbolic regression module uses deep reinforcement learning to infer analytical physical laws that fit data points, searching in the space of functional forms. -
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RL Baselines Zoo
A collection of 100+ pre-trained RL agents using Stable Baselines
RL Baselines Zoo is a comprehensive training framework and collection of pre-trained RL agents using Stable-Baselines3. It offers tools for training, tuning, and evaluating RL algorithms across many standard environments, including MuJoCo, Atari, and robotics simulations. Designed for reproducible RL research and benchmarking, it includes scripts, hyperparameter presets, and best practices for training robust agents. -
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RL Baselines3 Zoo
Training framework for Stable Baselines3 reinforcement learning agents
rl-baselines3-zoo is a collection of pre-trained models, benchmarks, and hyperparameter tuning tools built on top of Stable Baselines3, a reinforcement learning library. It provides an easy way to test, evaluate, and train RL agents across a wide variety of environments. -
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RL Poker is a study project Java implementation of an e-soft on-policy Monte Carlo Texas Hold'em poker reinforcement learning algoritm with a feedforward neural network and backpropagation. It provides a graphical interface to monitor game rounds.
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RL++ is an easy to use modular open source library for Reinforcement Learning written in C++. It includes learning algorithms (TD, Sarsa, Q) as well as the implementation of value function representations (LookupTable, TileCoding, Neuronal Network).
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RL-POMDP is a Reinforcement Learning (RL) based algorithm to find approximate and satisfactory solution to POMDP problems. RL-POMDP is orders of magnitude faster than exact POMDP solver.
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RLCard
Reinforcement Learning / AI Bots in Card (Poker) Games
RLCard is a toolkit for reinforcement learning research on card games. It includes several popular card games and focuses on learning algorithms for imperfect information games like poker and blackjack. -
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Based on the rl-ggp project. Research & development project on General Game Playing and relational reinforcement learning.
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RWARE
MuA multi-agent reinforcement learning environment
robotic-warehouse is a simulation environment and framework for robotic warehouse automation, enabling research and development of AI and robotic agents to manage warehouse logistics, such as item picking and transport. -
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Rainbow
Rainbow: Combining Improvements in Deep Reinforcement Learning
Combining improvements in deep reinforcement learning. Results and pretrained models can be found in the releases. Data-efficient Rainbow can be run using several options (note that the "unbounded" memory is implemented here in practice by manually setting the memory capacity to be the same as the maximum number of timesteps). -
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RecNN
Reinforced Recommendation toolkit built around pytorch 1.7
This is my school project. It focuses on Reinforcement Learning for personalized news recommendation. The main distinction is that it tries to solve online off-policy learning with dynamically generated item embeddings. I want to create a library with SOTA algorithms for reinforcement learning recommendation, providing the level of abstraction you like. -
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Reinforcement Learning Course Materials
Lecture notes, tutorial tasks including solutions
Lecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University. The source code for the entire course material is open and everyone is cordially invited to use it for self-learning (students) or to set up their own course (lecturers). -
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ReinforcementLearning.jl
A reinforcement learning package for Julia
A collection of tools for doing reinforcement learning research in Julia. Provide elaborately designed components and interfaces to help users implement new algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate and diagnose agents. Facilitate reproducibility from traditional tabular methods to modern deep reinforcement learning algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate and diagnose agents. Facilitate reproducibility from traditional tabular methods to modern deep reinforcement learning algorithms. Provide elaborately designed components and interfaces to help users implement new algorithms. A number of built-in environments and third-party environment wrappers are provided to evaluate algorithms in various scenarios. -
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ReinforcementLearningAnIntroduction.jl
Julia code for the book Reinforcement Learning An Introduction
This project provides the Julia code to generate figures in the book Reinforcement Learning: An Introduction(2nd). One of our main goals is to help users understand the basic concepts of reinforcement learning from an engineer's perspective. Once you have grasped how different components are organized, you're ready to explore a wide variety of modern deep reinforcement learning algorithms in ReinforcementLearningZoo.jl. -
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ReinventCommunity
Jupyter Notebook tutorials for REINVENT 3.2
This repository is a collection of useful jupyter notebooks, code snippets and example JSON files illustrating the use of Reinvent 3.2. -
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SLM Lab
Modular Deep Reinforcement Learning framework in PyTorch
SLM Lab is a modular and extensible deep reinforcement learning framework designed for research and practical applications. It provides implementations of various state-of-the-art RL algorithms and emphasizes reproducibility, scalability, and detailed experiment tracking. SLM Lab is structured around a flexible experiment management system, allowing users to define, run, and analyze RL experiments efficiently. -
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SMAC
SMAC: The StarCraft Multi-Agent Challenge
SMAC (StarCraft II Multi-Agent Challenge) is a benchmark environment for cooperative multi-agent reinforcement learning (MARL), based on real-time strategy (RTS) game scenarios in StarCraft II. It allows researchers to test algorithms where multiple units (agents) must collaborate to win battles against built-in game AI opponents. SMAC provides a controlled testbed for studying decentralized execution and centralized training paradigms in MARL. -
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Stable Baselines
A fork of OpenAI Baselines, implementations of reinforcement learning
Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. You can read a detailed presentation of Stable Baselines in the Medium article. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details. -
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T81 558
Applications of Deep Neural Networks
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. -
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TaskWeaver
A code-first agent framework for seamlessly planning analytics tasks
TaskWeaver is a multi-agent AI framework designed for orchestrating autonomous agents that collaborate to complete complex tasks. -
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The Teachingbox uses advanced machine learning techniques to relieve developers from the programming of hand-crafted sophisticated behaviors of autonomous agents (such as robots, game players etc...) In the current status we have implemented a well founded reinforcement learning core in Java with many popular usecases, environments, policies and learners. Obtaining the teachingbox: FOR USERS: If you want to download the latest releases, please visit: http://search.maven.org/#search|ga|1|teachingbox FOR DEVELOPERS: 1) If you use Apache Maven, just add the following dependency to your pom.xml: <dependency> <groupId>org.sf.teachingbox</groupId> <artifactId>teachingbox-core</artifactId> <version>1.2.3</version> </dependency> 2) If you want to check out the most recent source-code: git clone https://git.code.sf.net/p/teachingbox/core teachingbox-core Documentation: https://sourceforge.net/p/teachingbox/documentation/HEAD/tree/trunk/manual/
