# Reinforcement Learning Agent Libraries

> Search results for `reinforcement learning library for training agents` on awesome-repositories.com. 104 total matches; showing the first 50.

Explore on the web: https://awesome-repositories.com/q/reinforcement-learning-library-for-training-agents

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## Results

- [morvanzhou/reinforcement-learning-with-tensorflow](https://awesome-repositories.com/repository/morvanzhou-reinforcement-learning-with-tensorflow.md) (9,464 ⭐) — This project is an educational repository of reinforcement learning agents and tutorials implemented using TensorFlow. It provides a practical codebase for both model-free and model-based learning agents, designed to demonstrate how AI agents learn through trial and error.

The collection features detailed implementations of various algorithmic approaches, including Deep Q-Networks and Policy Gradient methods. It specifically covers Actor-Critic architectures for continuous and discrete action spaces, alongside Proximal Policy Optimization and Deep Deterministic Policy Gradients.

The framewor
- [dennybritz/reinforcement-learning](https://awesome-repositories.com/repository/dennybritz-reinforcement-learning.md) (22,039 ⭐) — This repository provides a comprehensive library of reinforcement learning algorithms designed for training autonomous agents. It serves as a research-oriented collection of implementations that cover fundamental decision-making strategies, including dynamic programming, temporal difference learning, and policy gradient methods.

The project distinguishes itself by offering specialized frameworks for deep reinforcement learning and structured decision modeling. It includes implementations for deep Q-learning that utilize neural networks, experience replay, and prioritized sampling to approxima
- [mathfoundationrl/book-mathematical-foundation-of-reinforcement-learning](https://awesome-repositories.com/repository/mathfoundationrl-book-mathematical-foundation-of-reinforcement-learning.md) (16,543 ⭐) — This project is an educational resource designed to teach the mathematical foundations and core algorithms of reinforcement learning. It provides a structured academic curriculum that combines textbooks, lecture materials, and practical code examples to guide learners through the principles of Markov decision processes and reinforcement learning theory.

The repository distinguishes itself by integrating a grid-based simulation framework that allows users to test algorithms within custom environments. This environment supports the analysis of agent performance by rendering state values, polici
- [d2l-ai/d2l-en](https://awesome-repositories.com/repository/d2l-ai-d2l-en.md) (29,001 ⭐) — This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation.

The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex
- [microsoft/airsim](https://awesome-repositories.com/repository/microsoft-airsim.md) (17,956 ⭐) — AirSim is a high-fidelity simulation platform designed for the development and testing of autonomous vehicles. Built as a plugin for game engines, it provides a physics-based environment that models vehicle dynamics and sensor data, serving as a foundation for robotics research, computer vision training, and reinforcement learning.

The platform distinguishes itself through its support for hardware-in-the-loop and software-in-the-loop testing, allowing developers to validate control logic and firmware against real-world signals or concurrent processes. It offers extensive programmatic control
- [shangtongzhang/reinforcement-learning-an-introduction](https://awesome-repositories.com/repository/shangtongzhang-reinforcement-learning-an-introduction.md) (14,569 ⭐) — This project is a Python-based educational framework designed to simulate reinforcement learning algorithms and environments. It serves as a platform for reproducing classic textbook examples, allowing users to study agent behavior, policy improvement, and the fundamental mechanics of decision-making in controlled settings.

The library provides implementations for core reinforcement learning concepts, including temporal difference learning, Monte Carlo episode sampling, and tabular value function approximation. It enables the analysis of specific algorithmic behaviors, such as identifying and
- [ljpzzz/machinelearning](https://awesome-repositories.com/repository/ljpzzz-machinelearning.md) (8,706 ⭐) — This project is a machine learning implementation library featuring a collection of code examples that implement supervised, unsupervised, and reinforcement learning algorithms from scratch. It provides a comprehensive set of toolkits for core machine learning components, including a natural language processing toolkit, a reinforcement learning framework, and suites for data dimensionality reduction and pattern mining.

The library includes specialized implementations for reinforcement learning, such as Q-Learning, Deep Q-Networks, and Actor-Critic agents. The natural language processing capab
- [microsoft/ai-agents-for-beginners](https://awesome-repositories.com/repository/microsoft-ai-agents-for-beginners.md) (67,369 ⭐) — This project is a structured educational resource and technical guide for designing and implementing autonomous systems using large language models. It provides a comprehensive curriculum and code samples focused on agentic design patterns, autonomous development, and the creation of systems capable of planning and executing multi-step tasks.

The resource details the implementation of agentic retrieval-augmented generation, where models autonomously plan and refine data searches. It covers a wide array of orchestrators and design patterns, including metacognitive reflection for self-correctin
- [isaac-sim/isaacgymenvs](https://awesome-repositories.com/repository/isaac-sim-isaacgymenvs.md) (2,942 ⭐) — IsaacGymEnvs is a GPU-accelerated physics sandbox and robotics policy training suite designed for reinforcement learning. It serves as a vectorized robotic simulator that runs thousands of parallel environments on GPUs to accelerate the training of neural networks.

The project provides a sim-to-real transfer framework that utilizes domain randomization and physics variations to ensure policies trained in simulation are robust enough for deployment on real hardware. It distinguishes itself through a high-performance architecture that uses tensor-based state management to handle observations an
- [microsoft/agent-lightning](https://awesome-repositories.com/repository/microsoft-agent-lightning.md) (15,047 ⭐) — Agent Lightning is an optimization framework designed to refine the performance of individual AI agents within complex multi-agent systems. It provides a platform for improving decision-making and task execution by applying reinforcement learning, supervised fine-tuning, and automated prompt optimization.

The framework distinguishes itself through its ability to isolate specific agents for targeted tuning, allowing developers to enhance individual behaviors while maintaining the stability of the broader system architecture. By utilizing a modular interface, it integrates with diverse agent fr
- [ageron/handson-ml2](https://awesome-repositories.com/repository/ageron-handson-ml2.md) (29,938 ⭐) — This project provides a collection of practical machine learning code examples, including implementations for supervised, unsupervised, and reinforcement learning algorithms. It features deep learning model implementations for convolutional, recurrent, and generative architectures, alongside specific examples of reinforcement learning agents that maximize rewards in simulated environments.

The repository includes dedicated data preprocessing pipelines for sanitization, feature scaling, and dimensionality reduction. It also provides implementations for a wide range of specific models, such as
- [dlr-rm/stable-baselines3](https://awesome-repositories.com/repository/dlr-rm-stable-baselines3.md) (12,765 ⭐) — Stable-baselines3 is a reinforcement learning library built on the PyTorch deep learning framework. It provides a collection of reliable, standardized implementations of reinforcement learning algorithms designed for training, testing, and benchmarking agent policies in diverse simulated environments.

The library functions as an agent training toolkit that emphasizes modularity and reproducibility. It features a unified environment interface and supports vectorized execution to accelerate data collection across multiple simulation instances. Users can customize neural network architectures, f
- [unity-technologies/ml-agents](https://awesome-repositories.com/repository/unity-technologies-ml-agents.md) (19,494 ⭐) — This project is a reinforcement learning toolkit and simulation-based AI trainer for creating intelligent agents within Unity simulations. It provides a multi-agent simulation framework for configuring cooperative or competitive scenarios and includes an environment wrapper that bridges simulations with standard machine learning libraries using gym-style interfaces.

The system features a native cross-platform inference engine that executes trained neural network models for real-time decision making without external dependencies. It enables the acceleration of the learning process by running m
- [rlcode/reinforcement-learning](https://awesome-repositories.com/repository/rlcode-reinforcement-learning.md) (3,642 ⭐) — Minimal and Clean Reinforcement Learning Examples
- [carla-simulator/carla](https://awesome-repositories.com/repository/carla-simulator-carla.md) (14,072 ⭐) — CARLA is an autonomous driving simulator and research environment designed for developing and validating self-driving software. It functions as an urban traffic simulator that generates realistic vehicle and pedestrian behavior and as a synthetic sensor data generator producing LiDAR, Radar, and camera data.

The platform distinguishes itself through its deep integration with robotics frameworks, specifically providing native connectivity to ROS2 nodes for robotic control and data processing. It supports the training of driving models via imitation and reinforcement learning within a controlle
- [thinking-machines-lab/tinker-cookbook](https://awesome-repositories.com/repository/thinking-machines-lab-tinker-cookbook.md) (2,856 ⭐) — Tinker Cookbook is an open-source framework for fine-tuning large language models, supporting supervised learning, reinforcement learning, and parameter-efficient techniques like LoRA adapters. It provides a complete pipeline for aligning models with human preferences through multi-stage RLHF workflows, from supervised fine-tuning through preference optimization to reinforcement learning.

The framework distinguishes itself through recipe-based training orchestration, where fine-tuning workflows are defined as composable recipe files that chain data loading, model configuration, and training l
- [morvanzhou/tutorials](https://awesome-repositories.com/repository/morvanzhou-tutorials.md) (12,952 ⭐) — This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization.

The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad
- [enggen/deepmind-advanced-deep-learning-and-reinforcement-learning](https://awesome-repositories.com/repository/enggen-deepmind-advanced-deep-learning-and-reinforcement-learning.md) (862 ⭐) — Advanced Deep Learning and Reinforcement Learning course taught at UCL in partnership with Deepmind
- [tensorflow/models](https://awesome-repositories.com/repository/tensorflow-models.md) (77,663 ⭐) — This repository serves as a centralized collection of state-of-the-art deep learning architectures and reference implementations designed for research and application development. It provides a comprehensive toolkit for computer vision and natural language processing, offering pre-built models and training pipelines for tasks ranging from image classification and object detection to complex sequence modeling.

The project distinguishes itself by providing a flexible execution harness that manages the entire training lifecycle, including data ingestion and backpropagation. It supports scalable
- [karpathy/convnetjs](https://awesome-repositories.com/repository/karpathy-convnetjs.md) (11,171 ⭐) — ConvNetJS is a JavaScript deep learning library and neural network training engine designed for client-side machine learning. It functions as a framework for building, training, and running convolutional neural networks directly within a web browser without the need for a backend server.

The library specializes in image recognition and pattern analysis using convolutional and pooling layers. It enables the creation of models for classification and regression tasks, as well as the development of reinforcement learning agents that optimize behavior through trial and error in simulated environme
- [thuml/transfer-learning-library](https://awesome-repositories.com/repository/thuml-transfer-learning-library.md) (3,917 ⭐) — Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
- [haosulab/maniskill](https://awesome-repositories.com/repository/haosulab-maniskill.md) (2,576 ⭐) — ManiSkill is a GPU-accelerated robot simulation framework designed for training robotic manipulation skills, benchmarking learning algorithms, and generating synthetic datasets. It serves as a reinforcement learning environment where robot control policies can be developed and evaluated using parallelized physics and rendering on the GPU.

The platform is distinguished by its ability to perform sim-to-real transfer, allowing policies trained in virtual environments to be deployed onto physical robotic hardware. It features ray-traced parallel rendering for producing high-frame-rate RGBD and se
- [deepmind/lab](https://awesome-repositories.com/repository/deepmind-lab.md) (7,365 ⭐) — Lab is a customizable 3D platform and research testbed designed for training and testing autonomous agents using reinforcement learning. It serves as a spatial AI training simulator where agents can be evaluated through navigation and puzzle-solving tasks.

The environment allows for the definition of complex layouts and task behaviors through external scripting, enabling the generation of specific challenges for AI research. It supports both automated training via standard API bindings and manual agent control to validate simulation dynamics.

The system utilizes a grid-based spatial represen
- [0russwest0/agent-r1](https://awesome-repositories.com/repository/0russwest0-agent-r1.md) (1,500 ⭐) — Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning
- [p-christ/deep-reinforcement-learning-algorithms-with-pytorch](https://awesome-repositories.com/repository/p-christ-deep-reinforcement-learning-algorithms-with-pytorch.md) (5,935 ⭐) — This is a PyTorch-based toolkit for training reinforcement learning agents, providing implementations of standard and hierarchical deep RL algorithms. It is designed as a library for deep reinforcement learning research and experimentation, supporting both discrete and continuous control tasks through a collection of algorithm implementations.

The project distinguishes itself by offering a hierarchical reinforcement learning framework that decomposes complex long-horizon tasks into manageable sub-goals using meta-controllers and lower-level policies. It also includes a Hindsight Experience Re
- [letianzj/quantresearch](https://awesome-repositories.com/repository/letianzj-quantresearch.md) (2,808 ⭐) — QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial time-series analysis, and systematic trading. It provides an event-driven backtesting environment for validating strategies against historical tick and bar data, alongside a dedicated portfolio optimization engine for calculating asset weights and risk metrics.

The project distinguishes itself through a machine learning finance toolkit that implements recurrent neural networks for price prediction and reinforcement learning for derivative pricing. It also features advanced statisti
- [camel-ai/camel](https://awesome-repositories.com/repository/camel-ai-camel.md) (17,253 ⭐) — This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer.

The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva
- [alibaba/roll](https://awesome-repositories.com/repository/alibaba-roll.md) (2,844 ⭐) — ROLL is a distributed reinforcement learning framework and model alignment toolkit designed for large language models. It serves as a scalable training pipeline and GPU cluster manager, providing the infrastructure to align model behavior using reinforcement learning algorithms and preference optimization techniques.

The project distinguishes itself through an agentic rollout orchestrator that generates and collects multi-turn interaction trajectories between AI agents and simulated environments. It supports specialized alignment methods including Direct Preference Optimization, reinforcement
- [tensorflow/agents](https://awesome-repositories.com/repository/tensorflow-agents.md) (3,016 ⭐) — TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
- [eleutherai/gpt-neo](https://awesome-repositories.com/repository/eleutherai-gpt-neo.md) (8,275 ⭐) — GPT-Neo is an open-source distributed training framework designed for scaling GPT-2 and GPT-3-style language models across multiple devices using mesh-tensorflow for model parallelism. It provides the infrastructure to train transformer-based language models with billions of parameters across distributed computing environments, making large-scale language model research accessible outside of proprietary systems.

The framework supports training both autoregressive GPT-style models and masked language models like BERT or RoBERTa, with configurable masking strategies and token handling. It inclu
- [isaac-sim/isaaclab](https://awesome-repositories.com/repository/isaac-sim-isaaclab.md) (6,377 ⭐) — Isaac Lab is an open-source framework for training robot policies in physically simulated environments, supporting both single-agent and multi-agent reinforcement learning. It is built on an Omniverse-PhysX simulation backend that models rigid bodies, articulated systems, deformable objects, and sensors, and provides a task-based environment configuration system where each training environment is defined as a modular class specifying observation spaces, action spaces, reward functions, and termination conditions.

The framework distinguishes itself through an RL-library abstraction layer that
- [lywangpx/reinforcement-learning-2nd-edition-by-sutton-exercise-solutions](https://awesome-repositories.com/repository/lywangpx-reinforcement-learning-2nd-edition-by-sutton-exercise-solutions.md) (2,417 ⭐) — Solutions of Reinforcement Learning, An Introduction
- [huggingface/trl](https://awesome-repositories.com/repository/huggingface-trl.md) (18,653 ⭐) — This library provides a comprehensive framework for fine-tuning, aligning, and distilling transformer-based language models. It serves as a toolkit for adapting models to specialized domains through supervised learning, while offering advanced methodologies to improve output quality and reasoning capabilities.

The project distinguishes itself through specialized alignment and optimization techniques, including direct preference optimization and reinforcement learning, which allow models to be tuned against human preferences without complex reward modeling. It further supports training efficie
- [eriklindernoren/ml-from-scratch](https://awesome-repositories.com/repository/eriklindernoren-ml-from-scratch.md) (31,918 ⭐) — This project is an educational toolkit that provides implementations of fundamental machine learning algorithms built from scratch. By avoiding high-level library abstractions, it serves as a pedagogical reference for understanding the mathematical foundations and core mechanics of supervised learning, unsupervised learning, and reinforcement learning models.

The repository distinguishes itself through a modular approach to model construction, allowing users to build custom neural networks by chaining independent functional blocks. It covers a wide range of techniques, including gradient-base
- [masworks/ml-agent](https://awesome-repositories.com/repository/masworks-ml-agent.md) (62 ⭐) — The official implementation of "ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering"
- [panaversity/learn-agentic-ai](https://awesome-repositories.com/repository/panaversity-learn-agentic-ai.md) (3,908 ⭐) — This project is an educational curriculum and architectural framework for building autonomous AI agents and multi-agent systems. It provides a structured learning path focused on the development of independent software components capable of planning, executing tasks, and utilizing external tools to achieve high-level goals.

The framework emphasizes multi-agent system orchestration through distributed architectures where specialized agents collaborate using standardized communication protocols. It details specific design patterns such as dual-memory systems for maintaining short-term plans and
- [openai/gym](https://awesome-repositories.com/repository/openai-gym.md) (37,223 ⭐) — Gym is a reinforcement learning environment toolkit and agent simulation framework. It provides a standardized API and a universal communication interface that defines how learning agents interact with simulation environments through actions and observations.

The project includes a benchmark environment suite and a diverse library of pre-configured simulation worlds, including physics engines and classic control tasks. It enables the creation of custom simulation environments to train agents in specific operational scenarios while ensuring reproducibility across different learning algorithms.
- [crewaiinc/crewai](https://awesome-repositories.com/repository/crewaiinc-crewai.md) (53,687 ⭐) — CrewAI is a multi-agent orchestration framework designed for building autonomous systems that execute complex, multi-step workflows. It provides a development platform where specialized agents are defined with specific roles, goals, and tool sets to perform tasks collaboratively. By leveraging a declarative workflow engine, the system manages task dependencies, state transitions, and execution logic, allowing for the creation of structured, stateful sequences of operations.

The framework distinguishes itself through its hierarchical management capabilities, which utilize manager agents to coo
- [kubescape/kubescape](https://awesome-repositories.com/repository/kubescape-kubescape.md) (11,489 ⭐) — Kubescape is a Kubernetes security posture management platform designed to scan clusters, manifests, and images for misconfigurations, vulnerabilities, and compliance risks. It functions as a comprehensive security suite incorporating a compliance scanner, a container image vulnerability scanner, an admission controller for policy enforcement, and a runtime security monitor.

The platform distinguishes itself through runtime-aware vulnerability filtering, which maps libraries loaded in memory to determine if vulnerabilities are actually reachable. It also integrates with AI assistants via a Mo
- [sanbuphy/learn-coding-agent](https://awesome-repositories.com/repository/sanbuphy-learn-coding-agent.md) (12,034 ⭐) — This project is a framework for building AI coding agents that automate software development tasks using large language models. It includes a task lifecycle manager that tracks complex development goals through a persistent graph of dependent tasks and a system for multi-agent orchestration to delegate tasks to specialized sub-agents.

The framework implements a Model Context Protocol client to discover and execute tools from external servers and provides a remote development bridge to synchronize local command line interfaces with remote containers or desktop environments.

The system covers
- [zai-org/open-autoglm](https://awesome-repositories.com/repository/zai-org-open-autoglm.md) (23,532 ⭐) — Open-AutoGLM is an autonomous agent framework designed to perform complex user workflows on mobile devices. By translating natural language instructions into precise sequences of taps, scrolls, and text inputs, the system enables the automation of mobile application interactions and testing.

The platform distinguishes itself through a combination of vision-language processing and reinforcement learning. It converts graphical user interfaces into structured data, allowing agents to parse screen elements and map natural language commands to coordinate-based actions. To ensure reliability, the s
- [valarzz/model-based-reinforcement-learning-for-parameterized-action-spaces](https://awesome-repositories.com/repository/valarzz-model-based-reinforcement-learning-for-parameterized-action-spaces.md) (0 ⭐) — Source code for DLPA, ICML 2024
- [ai4finance-foundation/finrl](https://awesome-repositories.com/repository/ai4finance-foundation-finrl.md) (13,964 ⭐) — FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated trading strategies. It functions as a quantitative finance toolkit that integrates deep learning algorithms with financial market simulations to address complex portfolio management and asset allocation tasks. The platform provides an end-to-end pipeline for transforming raw market data into actionable trading models.

The project distinguishes itself through a layered, modular architecture that separates data processing, environment simulation, and agent training. This design allow
- [qhmiao/p-m-for-continual-learning](https://awesome-repositories.com/repository/qhmiao-p-m-for-continual-learning.md) (6 ⭐) — [NeurIPS 2025] Official Implementation for Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning
- [raaminz/training](https://awesome-repositories.com/repository/raaminz-training.md) (28 ⭐) — This Repository is all about my training classes
- [expo/expo](https://awesome-repositories.com/repository/expo-expo.md) (50,111 ⭐) — Expo is a universal mobile framework designed to build native iOS and Android applications from a single codebase using web-standard technologies. It provides a comprehensive development environment that includes a unified runtime for testing, cloud-based infrastructure for compiling and signing native binaries, and automated tools for managing the entire mobile release lifecycle, including app store submission.

The framework distinguishes itself through a plugin-based native configuration engine that programmatically modifies project files, allowing developers to integrate native modules wit
- [abhineet123/deep-learning-for-tracking-and-detection](https://awesome-repositories.com/repository/abhineet123-deep-learning-for-tracking-and-detection.md) (2,508 ⭐) — This project is a curated research repository and structured index focused on deep learning techniques for object detection and tracking. It serves as a centralized archive for academic papers, datasets, and software implementations, providing a cohesive resource for studying methodologies used in image and video analysis.

The repository distinguishes itself through a systematic approach to knowledge management, utilizing hierarchical file organization and metadata-driven tagging to categorize technical literature. By indexing domain-specific datasets and cross-referencing academic resources,
- [rednaga/training](https://awesome-repositories.com/repository/rednaga-training.md) (431 ⭐) — Training materials crafted and publicly provided by Red Naga members
- [azuread/microsoft-authentication-library-for-js](https://awesome-repositories.com/repository/azuread-microsoft-authentication-library-for-js.md) (4,084 ⭐) — Microsoft Authentication Library (MSAL) for JS
- [elastic/elasticsearch](https://awesome-repositories.com/repository/elastic-elasticsearch.md) (77,012 ⭐) — Elasticsearch is a distributed search engine and document store designed for the high-performance indexing and retrieval of massive volumes of unstructured data. It functions as a centralized analytics platform, providing a schema-flexible architecture that organizes information into searchable indices while maintaining global cluster state through a distributed consensus mechanism.

The platform distinguishes itself through its integrated approach to observability, security, and advanced analytics. It combines full-text, vector, and hybrid search capabilities with machine learning-driven insi