Search Results for "reinforcement learning"
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Showing 301 open source projects for "reinforcement learning"
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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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Behaviour Suite Reinforcement Learning
bsuite is a collection of carefully-designed experiments
bsuite is a research framework developed by Google DeepMind that provides a comprehensive collection of experiments for evaluating the core capabilities of reinforcement learning (RL) agents. Its main goal is to identify, measure, and analyze fundamental aspects of learning efficiency and generalization in RL algorithms. The library enables researchers to benchmark their agents on standardized tasks, facilitating reproducible and transparent comparisons across different approaches. Each experiment in bsuite is meticulously designed to capture key challenges in RL, such as exploration, credit assignment, and stability. ... -
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Tensorforce
A TensorFlow library for applied reinforcement learning
Tensorforce is an open-source deep reinforcement learning framework built on TensorFlow, emphasizing modularized design and straightforward usability for applied research and practice. -
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Best-of Machine Learning with Python
A ranked list of awesome machine learning Python libraries
This curated list contains 900 awesome open-source projects with a total of 3.3M stars grouped into 34 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! General-purpose machine learning and deep learning... -
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OpenRLHF
An Easy-to-use, Scalable and High-performance RLHF Framework
OpenRLHF is an easy-to-use, scalable, and high-performance framework for Reinforcement Learning with Human Feedback (RLHF). It supports various training techniques and model architectures. -
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Gymnasium
An API standard for single-agent reinforcement learning environments
Gymnasium is a fork of OpenAI Gym, maintained by the Farama Foundation, that provides a standardized API for reinforcement learning environments. It improves upon Gym with better support, maintenance, and additional features while maintaining backward compatibility. -
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Alibi Explain
Algorithms for explaining machine learning models
Alibi is a Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models. -
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BindsNET
Simulation of spiking neural networks (SNNs) using PyTorch
A Python package used for simulating spiking neural networks (SNNs) on CPUs or GPUs using PyTorch Tensor functionality. BindsNET is a spiking neural network simulation library geared towards the development of biologically inspired algorithms for machine learning. This package is used as part of ongoing research on applying SNNs to machine learning (ML) and reinforcement learning (RL) problems in the Biologically Inspired Neural & Dynamical Systems (BINDS) lab. -
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ViZDoom
Doom-based AI research platform for reinforcement learning
...Access to the depth buffer (3D vision). Automatic labeling of game objects visible in the frame. Access to the list of actors/objects and map geometry.ViZDoom API is reinforcement learning friendly (suitable also for learning from demonstration, apprenticeship learning or apprenticeship via inverse reinforcement learning. -
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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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AgentUniverse
agentUniverse is a LLM multi-agent framework
AgentUniverse is a multi-agent AI framework that enables coordination between multiple intelligent agents for complex task execution and automation. -
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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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Agent Reinforcement Trainer
Train multi-step agents for real-world tasks using GRPO
Agent Reinforcement Trainer, or ART is an open-source reinforcement learning framework tailored to training large language model agents through experience, making them more reliable and performant on multi-turn, multi-step tasks. Instead of just manually crafting prompts or relying on supervised fine-tuning, ART uses techniques like Group Relative Policy Optimization (GRPO) to let agents learn from environmental feedback and reward signals. -
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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. -
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OpenSpiel
Environments and algorithms for research in general reinforcement
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully- observable) grid worlds and social dilemmas. -
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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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Vowpal Wabbit
Machine learning system which pushes the frontier of machine learning
Vowpal Wabbit is a machine learning system that pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. There is a specific focus on reinforcement learning with several contextual bandit algorithms implemented and the online nature lending to the problem well. Vowpal Wabbit is a destination for implementing and maturing state-of-the-art algorithms with performance in mind. ... -
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Godot RL Agents
An Open Source package that allows video game creators
godot_rl_agents is a reinforcement learning integration for the Godot game engine. It allows AI agents to learn how to interact with and play Godot-based games using RL algorithms. The toolkit bridges Godot with Python-based RL libraries like Stable-Baselines3, making it possible to create complex and visually rich RL environments natively in Godot. -
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Multi-Agent Orchestrator
Flexible and powerful framework for managing multiple AI agents
Multi-Agent Orchestrator is an AI coordination framework that enables multiple intelligent agents to work together to complete complex, multi-step workflows. -
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TorchRL
A modular, primitive-first, python-first PyTorch library
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. TorchRL provides PyTorch and python-first, low and high-level abstractions for RL that are intended to be efficient, modular, documented, and properly tested. The code is aimed at supporting research in RL. Most of it is written in Python in a highly modular way, such that researchers can easily swap components, transform them, or write new ones with little effort. -
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Stable Baselines3
PyTorch version of Stable Baselines
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines. You can read a detailed presentation of Stable Baselines3 in the v1.0 blog post or our JMLR paper. 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. -
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Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. ... -
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AI4U
Multi-engine plugin to specify agents with reinforcement learning
AI4U is a multi-engine plugin (Godot and Unity) that allows you to design Non-Player Characters (NPCs) of games using an agent abstraction. In addition, AI4U has a low-level API that allows you to connect the agent to any algorithm made available in Python by the reinforcement learning community specifically and by the Artificial Intelligence community in general. Reinforcement learning promises to overcome traditional navigation mesh mechanisms in games and to provide more autonomous characters. AI4U can be integrated into Imitation Learning through Behavioral Cloning or Generative Adversarial Imitation Learning present on stable-baslines. ... -
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EnvPool
C++-based high-performance parallel environment execution engine
EnvPool is a fast, asynchronous, and parallel RL environment library designed for scaling reinforcement learning experiments. Developed by SAIL at Singapore, it leverages C++ backend and Python frontend for extremely high-speed environment interaction, supporting thousands of environments running in parallel on a single machine. It's compatible with Gymnasium API and RLlib, making it suitable for scalable training pipelines. -
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LightZero
[NeurIPS 2023 Spotlight] LightZero
LightZero is an efficient, scalable, and open-source framework implementing MuZero, a powerful model-based reinforcement learning algorithm that learns to predict rewards and transitions without explicit environment models. Developed by OpenDILab, LightZero focuses on providing a highly optimized and user-friendly platform for both academic research and industrial applications of MuZero and similar algorithms.
