{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"skrl is a modular reinforcement learning library that implements algorithms in PyTorch, JAX, and NVIDIA Warp, supporting Gymnasium, PettingZoo, ManiSkill, Isaac Lab, and MuJoCo environments with multi-scope agent training.","skillfed_tags":["reinforcement-learning","multi-backend","modular-agents"],"use_cases":["Train RL agents on Gymnasium environments (CartPole, Atari, MuJoCo) with a clean, algorithm-agnostic API.","Develop and test new RL algorithms in a transparent, readable codebase without reimplementing environment boilerplate.","Run multi-agent or multi-environment training in Isaac Lab or MuJoCo simulations with scope-based resource management.","Prototype RL solutions using PyTorch, then port to JAX or NVIDIA Warp for performance without rewriting agent logic.","Conduct research on algorithm variants with modular components for policies, value functions, and memory buffers."],"what_it_does":"skrl is a modular reinforcement learning library designed for clarity and transparency in algorithm implementation. It abstracts away boilerplate by providing reusable agent and environment components that work with PyTorch, JAX, or NVIDIA Warp backends, letting you focus on algorithm logic rather than framework plumbing. The library supports multiple environment interfaces\u2014Gymnasium, PettingZoo, ManiSkill, Isaac Lab, and MuJoCo\u2014and enables training multiple agent scopes (subsets of environments) in a single run, with optional resource sharing between scopes.\n\nTypical use involves wrapping your environment, instantiating an agent with a chosen algorithm (e.g., PPO, DQN), and running training loops. The modular design means you can swap algorithms, environments, and backends with minimal code changes. It is actively maintained and documented, with a published research paper backing its design philosophy.","worth_installing":"Yes, if you are developing or researching reinforcement learning algorithms. The modular design, support for multiple backends, and active maintenance make it a solid choice for RL projects. Install it if you want readable, transparent algorithm implementations and multi-environment training capabilities. Not necessary if you only need a simple single-algorithm solver or prefer a more opinionated framework."},"id":"skrl","links":{"html":"https://skillfed.io/packages/skrl","md":"https://skillfed.io/packages/skrl.md","pypi":"https://pypi.org/project/skrl/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-10","license_spdx":null,"license_treatment":"permissive","name":"skrl","python_support":"supports_current","summary":"Modular and flexible library for reinforcement learning on PyTorch and JAX"},"popularity":{"monthly_downloads":142261,"position":11219,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.1.0"}