Open Source Reinforcement Learning Frameworks
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Reinforcement Learning Frameworks
View 31 business solutionsBrowse free open source Reinforcement Learning Frameworks and projects below. Use the toggles on the left to filter open source Reinforcement Learning Frameworks by OS, license, language, programming language, and project status.
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DeepSeek R1
Open-source, high-performance AI model with advanced reasoning
DeepSeek-R1 is an open-source large language model developed by DeepSeek, designed to excel in complex reasoning tasks across domains such as mathematics, coding, and language. DeepSeek R1 offers unrestricted access for both commercial and academic use. The model employs a Mixture of Experts (MoE) architecture, comprising 671 billion total parameters with 37 billion active parameters per token, and supports a context length of up to 128,000 tokens. DeepSeek-R1's training regimen uniquely integrates large-scale reinforcement learning (RL) without relying on supervised fine-tuning, enabling the model to develop advanced reasoning capabilities. This approach has resulted in performance comparable to leading models like OpenAI's o1, while maintaining cost-efficiency. To further support the research community, DeepSeek has released distilled versions of the model based on architectures such as LLaMA and Qwen. -
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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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DeepSeek-V3
Powerful AI language model (MoE) optimized for efficiency/performance
DeepSeek-V3 is a robust Mixture-of-Experts (MoE) language model developed by DeepSeek, featuring a total of 671 billion parameters, with 37 billion activated per token. It employs Multi-head Latent Attention (MLA) and the DeepSeekMoE architecture to enhance computational efficiency. The model introduces an auxiliary-loss-free load balancing strategy and a multi-token prediction training objective to boost performance. Trained on 14.8 trillion diverse, high-quality tokens, DeepSeek-V3 underwent supervised fine-tuning and reinforcement learning to fully realize its capabilities. Evaluations indicate that it outperforms other open-source models and rivals leading closed-source models, achieving this with a training duration of 55 days on 2,048 Nvidia H800 GPUs, costing approximately $5.58 million. -
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AirSim
A simulator for drones, cars and more, built on Unreal Engine
AirSim is an open-source, cross platform simulator for drones, cars and more vehicles, built on Unreal Engine with an experimental Unity release in the works. It supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim's development is oriented towards the goal of creating a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles. For this purpose, AirSim also exposes APIs to retrieve data and control vehicles in a platform independent way. AirSim is fully enabled for multiple vehicles. This capability allows you to create multiple vehicles easily and use APIs to control them. -
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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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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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OSWorld
Benchmarking Multimodal Agents for Open-Ended Tasks
OSWorld is an open-source synthetic world environment designed for embodied AI research and multi-agent learning. It provides a richly simulated 3D world where multiple agents can interact, perform tasks, and learn complex behaviors. OSWorld emphasizes multi-modal interaction, enabling agents to process visual, auditory, and symbolic data for grounded learning in a simulated world. -
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms. Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework. Scale reinforcement learning (RL) with RLlib, a framework-agnostic RL library that ships with 30+ cutting-edge RL algorithms including A3C, DQN, and PPO. Easily build out scalable, distributed systems in Python with simple and composable primitives in Ray Core. -
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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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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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Brax
Massively parallel rigidbody physics simulation
Brax is a fast and fully differentiable physics engine for large-scale rigid body simulations, built on JAX. It is designed for research in reinforcement learning and robotics, enabling efficient simulations and gradient-based optimization. -
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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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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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highway-env
A minimalist environment for decision-making in autonomous driving
HighwayEnv is an OpenAI Gym-compatible environment focused on autonomous driving scenarios. It provides flexible simulations for testing decision-making algorithms in highway, intersection, and merging traffic situations. -
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H2O LLM Studio
Framework and no-code GUI for fine-tuning LLMs
Welcome to H2O LLM Studio, a framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs). You can also use H2O LLM Studio with the command line interface (CLI) and specify the configuration file that contains all the experiment parameters. To finetune using H2O LLM Studio with CLI, activate the pipenv environment by running make shell. With H2O LLM Studio, training your large language model is easy and intuitive. First, upload your dataset and then start training your model. Start by creating an experiment. You can then monitor and manage your experiment, compare experiments, or push the model to Hugging Face to share it with the community. -
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ML for Trading
Code for machine learning for algorithmic trading, 2nd edition
On over 800 pages, this revised and expanded 2nd edition demonstrates how ML can add value to algorithmic trading through a broad range of applications. Organized in four parts and 24 chapters, it covers the end-to-end workflow from data sourcing and model development to strategy backtesting and evaluation. Covers key aspects of data sourcing, financial feature engineering, and portfolio management. The design and evaluation of long-short strategies based on a broad range of ML algorithms, how to extract tradeable signals from financial text data like SEC filings, earnings call transcripts or financial news. Using deep learning models like CNN and RNN with financial and alternative data, and how to generate synthetic data with Generative Adversarial Networks, as well as training a trading agent using deep reinforcement learning. -
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Agent S
Agent S: an open agentic framework that uses computers like a human
Agent S is an open-source agentic framework designed to enable autonomous computer use through an Agent-Computer Interface (ACI). Built to operate graphical user interfaces like a human, it allows AI agents to perceive screens, reason about tasks, and execute actions across macOS, Windows, and Linux systems. The latest version, Agent S3, surpasses human-level performance on the OSWorld benchmark, demonstrating state-of-the-art results in complex multi-step computer tasks. Agent S combines powerful foundation models (such as GPT-5) with grounding models like UI-TARS to translate visual inputs into precise executable actions. It supports flexible deployment via CLI, SDK, or cloud, and integrates with multiple model providers including OpenAI, Anthropic, Gemini, Azure, and Hugging Face endpoints. With optional local code execution, reflection mechanisms, and compositional planning, Agent S provides a scalable and research-driven framework for building advanced computer-use agents. -
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Pwnagotchi
Deep Reinforcement learning instrumenting bettercap for WiFi pwning
Pwnagotchi is an A2C-based “AI” powered by bettercap and running on a Raspberry Pi Zero W that learns from its surrounding WiFi environment in order to maximize the crackable WPA key material it captures (either through passive sniffing or by performing deauthentication and association attacks). This material is collected on disk as PCAP files containing any form of handshake supported by hashcat, including full and half WPA handshakes as well as PMKIDs. Instead of merely playing Super Mario or Atari games like most reinforcement learning based “AI” (yawn), Pwnagotchi tunes its own parameters over time to get better at pwning WiFi things in the real world environments you expose it to. To give hackers an excuse to learn about reinforcement learning and WiFi networking, and have a reason to get out for more walks. -
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AndroidEnv
RL research on Android devices
android_env is a reinforcement learning (RL) environment developed by Google DeepMind that enables agents to interact with Android applications directly as a learning environment. It provides a standardized API for training agents to perform tasks on Android apps, supporting tasks ranging from games to productivity apps, making it suitable for research in real-world RL settings. -
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Cosmos-RL
Cosmos-RL is a flexible and scalable Reinforcement Learning framework
Cosmos-RL is a scalable reinforcement learning framework designed specifically for physical AI systems such as robotics, autonomous agents, and multimodal models. It provides a distributed training architecture that separates policy learning and environment rollout processes, enabling efficient and asynchronous reinforcement learning at scale. The framework supports multiple parallelism strategies, including tensor, pipeline, and data parallelism, allowing it to leverage large GPU clusters effectively. It is built with compatibility in mind, supporting popular model families such as LLaMA, Qwen, and diffusion-based world models, as well as integration with Hugging Face ecosystems. cosmos-rl also includes support for advanced RL algorithms, low-precision training, and fault-tolerant execution, making it suitable for large-scale production workloads. -
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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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TextWorld
TextWorld is a sandbox learning environment for the training
TextWorld is a learning environment designed to train reinforcement learning agents to play text-based games, where actions and observations are entirely in natural language. Developed by Microsoft Research, TextWorld focuses on language understanding, planning, and interaction in complex, narrative-driven environments. It generates games procedurally, enabling scalable testing of agents’ natural language processing and decision-making abilities. -
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Bullet Physics SDK
Real-time collision detection and multi-physics simulation for VR
This is the official C++ source code repository of the Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc. We are developing a new differentiable simulator for robotics learning, called Tiny Differentiable Simulator, or TDS. The simulator allows for hybrid simulation with neural networks. It allows different automatic differentiation backends, for forward and reverse mode gradients. TDS can be trained using Deep Reinforcement Learning, or using Gradient based optimization (for example LFBGS). In addition, the simulator can be entirely run on CUDA for fast rollouts, in combination with Augmented Random Search. This allows for 1 million simulation steps per second. It is highly recommended to use PyBullet Python bindings for improved support for robotics, reinforcement learning and VR. Use pip install pybullet and checkout the PyBullet Quickstart Guide. -
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EasyRL
Reinforcement learning (RL) tutorial series
easy-rl is a beginner-friendly reinforcement learning (RL) tutorial series and framework developed by Datawhale China. It provides educational resources and implementations of various RL algorithms to help new researchers and practitioners learn RL concepts. -
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Hands-on Unsupervised Learning
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
This repo contains the code for the O'Reilly Media, Inc. book "Hands-on Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data" by Ankur A. Patel. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied; this is where unsupervised learning comes in. Unsupervised learning can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow. With the hands-on examples and code provided, you will identify difficult-to-find patterns in data.
Open Source Reinforcement Learning Frameworks Guide
Open source reinforcement learning frameworks provide developers, researchers, and organizations with the tools needed to create, train, evaluate, and deploy reinforcement learning models. These frameworks support environments where an intelligent agent learns by interacting with its surroundings and improving its decision-making through rewards and penalties. By making the underlying source code publicly available, they enable greater transparency, customization, and collaboration while allowing users to adapt the framework to a wide range of machine learning projects.
Many open source reinforcement learning frameworks include features for environment simulation, policy optimization, model evaluation, distributed training, and experiment management. They often support multiple reinforcement learning algorithms, making it easier to compare different approaches and refine model performance over time. Integration with machine learning libraries, cloud infrastructure, visualization tools, and data processing platforms also helps streamline development workflows for both research and production environments.
As reinforcement learning continues to expand into industries such as robotics, manufacturing, finance, healthcare, logistics, and autonomous systems, open source frameworks have become an important part of AI development. Organizations can use them to accelerate experimentation, reduce development costs, and build solutions tailored to specific operational goals. Their flexibility and active community contributions make them valuable resources for teams seeking to advance reinforcement learning initiatives while maintaining control over their development process.
What Features Do Open Source Reinforcement Learning Frameworks Provide?
- Flexible training pipelines: Build, test, and refine reinforcement learning workflows for different environments and learning objectives.
- Multiple algorithm support: Access diverse reinforcement learning methods to address varied control, optimization, and decision-making challenges.
- Environment compatibility: Connect with simulation environments for consistent training, testing, and performance evaluation.
- Custom model configuration: Adjust architectures, hyperparameters, and learning settings to match unique project requirements.
- Distributed training capabilities: Accelerate large experiments by spreading workloads across multiple processors or computing resources.
- Performance monitoring: Track rewards, learning progress, and evaluation metrics throughout model development.
- Checkpoint management: Save and restore training states to continue experiments without restarting from the beginning.
- Hardware acceleration: Leverage supported processing hardware to improve training efficiency and reduce execution time.
- Experiment reproducibility: Maintain consistent configurations, random seeds, and settings for repeatable testing and reliable comparisons.
Different Types of Open Source Reinforcement Learning Frameworks
- General-purpose frameworks: Support multiple reinforcement learning algorithms across diverse environments and research objectives.
- Deep reinforcement learning frameworks: Focus on neural network-based learning for solving complex decision-making tasks.
- Distributed training frameworks: Enable scalable model training across multiple devices or computing resources.
- Multi-agent reinforcement learning frameworks: Help train multiple agents that cooperate, compete, or interact within shared environments.
- Offline reinforcement learning frameworks: Learn policies from previously collected datasets without requiring continuous environment interaction.
- Model-based reinforcement learning frameworks: Build predictive environment models to improve planning efficiency and reduce training requirements.
- Robotics-focused frameworks: Emphasize control, simulation, and physical interaction for autonomous robotic applications.
What Are the Advantages Provided by Open Source Reinforcement Learning Frameworks?
- Flexible customization: Developers can modify architectures, algorithms, and workflows to match specialized research or production requirements.
- Cost efficiency: Organizations reduce licensing expenses while expanding experimentation across multiple projects.
- Community collaboration: Shared improvements accelerate innovation and encourage continuous enhancements from contributors worldwide.
- Broad algorithm support: Multiple reinforcement learning approaches simplify testing different strategies for varied objectives.
- Transparency: Accessible source code helps teams understand model behavior, implementation details, and optimization opportunities.
- Integration capabilities: Frameworks connect with machine learning, simulation, analytics, and infrastructure tools for streamlined development.
- Scalability: Teams can expand training workloads across different computing environments as project demands increase.
- Faster experimentation: Built-in components reduce development time while simplifying iterative testing and performance evaluation.
Who Uses Open Source Reinforcement Learning Frameworks?
- AI researchers: Build, evaluate, and refine reinforcement learning methods using customizable open source frameworks.
- Academic institutions: Support teaching, experimentation, and collaborative research across reinforcement learning topics.
- Machine learning engineers: Develop intelligent decision-making systems for production and testing environments.
- Robotics teams: Train autonomous machines to improve movement, navigation, and task completion.
- Data science teams: Explore sequential decision models to solve complex optimization challenges.
- Research laboratories: Conduct controlled experiments while comparing reinforcement learning approaches and configurations.
- Product development teams: Create adaptive features that respond intelligently to changing user interactions.
- Autonomous vehicle developers: Simulate driving decisions before validating models in real-world environments.
How Much Do Open Source Reinforcement Learning Frameworks Cost?
Open source reinforcement learning frameworks are available across a wide range of pricing models, with many offering their core capabilities at no licensing cost. Organizations can download, modify, and deploy these frameworks without paying subscription fees, making them an attractive option for research institutions, startups, and development teams. However, the overall cost depends on factors such as infrastructure, computing resources, implementation complexity, and ongoing maintenance. Larger projects that require distributed training, extensive experimentation, or high-performance hardware may incur significantly higher operational expenses.
Although the framework itself may be free to use, businesses should account for indirect costs when planning adoption. Expenses may include cloud computing, specialized hardware, technical support, staff training, integration with existing tools, and model optimization. Some organizations also invest in consulting services or managed infrastructure to accelerate deployment and improve reliability. Evaluating both the upfront and long-term operational costs provides a more accurate understanding of the total investment required.
What Do Open Source Reinforcement Learning Frameworks Integrate With?
Open source reinforcement learning frameworks can integrate with machine learning platforms that manage model training, experimentation, and deployment. They also work with data processing tools that prepare training datasets, transform input data, and support feature engineering. Many frameworks connect with simulation environments, robotics platforms, and game engines that provide interactive environments for training and evaluating reinforcement learning agents. Integration with cloud infrastructure and container orchestration platforms allows teams to scale training workloads, manage resources, and automate deployment pipelines.
These frameworks can also connect with monitoring and observability tools that track training performance, resource usage, and experiment results. Version control platforms, workflow automation tools, and continuous integration and delivery solutions help streamline collaboration and model lifecycle management. Database systems, analytics platforms, and visualization tools support data storage, reporting, and performance analysis. Security, identity management, and API management solutions further improve governance by controlling access, securing integrations, and simplifying communication between connected applications.
What Are the Trends Relating to Open Source Reinforcement Learning Frameworks?
- Better support for large-scale distributed training improves experimentation speed and model scalability.
- Expanded compatibility with modern AI ecosystems simplifies development across multiple machine learning workflows.
- Improved benchmarking methods encourage more reliable performance comparisons across different reinforcement learning approaches.
- Growing interest in robotics increases demand for realistic simulation and training environments.
- More frameworks emphasize modular architectures that simplify customization and maintenance.
- Enhanced support for offline reinforcement learning broadens practical business applications.
- Increased focus on reproducibility encourages standardized evaluation methods and consistent research outcomes.
Getting Started With Open Source Reinforcement Learning Frameworks
Selecting the right open source reinforcement learning framework starts with identifying your project's objectives, deployment environment, and team expertise. Consider whether the framework supports the algorithms, environments, and training methods required for your use case. Evaluate compatibility with your existing technology stack, hardware, and machine learning workflows to minimize integration challenges.
Performance and scalability are equally important. Look for frameworks that efficiently handle distributed training, large datasets, and hardware acceleration if your workloads demand significant computing resources. Documentation quality, update frequency, community activity, and long-term maintenance can also influence productivity and future reliability. Finally, review licensing terms, customization options, debugging capabilities, and available evaluation tools to ensure the framework aligns with your organization's technical, operational, and compliance requirements. Testing several frameworks with a small proof of concept can provide valuable insight before making a long-term decision.
