# MushroomRL: Simplifying Reinforcement Learning Research

arXiv: https://arxiv.org/abs/2001.01102 · HTML: https://mlsanity.com/paper/2001.01102/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2020.

## Built on by (most cited first, 30 of 50)

- [Tianshou: a Highly Modularized Deep Reinforcement Learning Library](https://mlsanity.com/paper/2107.14171/) (2021)
- [d3rlpy: An Offline Deep Reinforcement Learning Library](https://mlsanity.com/paper/2111.03788/) (2021)
- [TorchRL: A data-driven decision-making library for PyTorch](https://mlsanity.com/paper/2306.00577/) (2023)
- [skrl: Modular and Flexible Library for Reinforcement Learning](https://mlsanity.com/paper/2202.03825/) (2022)
- [Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts](https://mlsanity.com/paper/2311.11385/) (2023)
- [Sharing Knowledge in Multi-Task Deep Reinforcement Learning](https://mlsanity.com/paper/2401.09561/) (2024)
- [LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion](https://mlsanity.com/paper/2311.02496/) (2023)
- [LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning](https://mlsanity.com/paper/2303.00599/) (2023)
- [Adaptive Rational Activations to Boost Deep Reinforcement Learning](https://mlsanity.com/paper/2102.09407/) (2021)
- [Robot Learning of Mobile Manipulation with Reachability Behavior Priors](https://mlsanity.com/paper/2203.04051/) (2022)
- [Value Iteration in Continuous Actions, States and Time](https://mlsanity.com/paper/2105.04682/) (2021)
- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction](https://mlsanity.com/paper/2209.13308/) (2022)
- [Boosting Object Representation Learning via Motion and Object Continuity](https://mlsanity.com/paper/2211.09771/) (2022)
- [Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning](https://mlsanity.com/paper/2402.03046/) (2024)
- [Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning](https://mlsanity.com/paper/2105.04962/) (2021)
- [A2C is a special case of PPO](https://mlsanity.com/paper/2205.09123/) (2022)
- [Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula](https://mlsanity.com/paper/2311.01642/) (2023)
- [Robust Value Iteration for Continuous Control Tasks](https://mlsanity.com/paper/2105.12189/) (2021)
- [Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies](https://mlsanity.com/paper/2305.10411/) (2023)
- [Continuous-Time Fitted Value Iteration for Robust Policies](https://mlsanity.com/paper/2110.01954/) (2021)
- [Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery](https://mlsanity.com/paper/2203.04120/) (2022)
- [Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning](https://mlsanity.com/paper/2403.02107/) (2024)
- [Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks](https://mlsanity.com/paper/2210.03325/) (2022)
- [XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library](https://mlsanity.com/paper/2312.16248/) (2023)
- [RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control](https://mlsanity.com/paper/2306.03530/) (2023)
- [Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learning](https://mlsanity.com/paper/2312.03764/) (2023)
- [CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms](https://mlsanity.com/paper/2111.08819/) (2021)
- [Dimensionality Reduction and Prioritized Exploration for Policy Search](https://mlsanity.com/paper/2203.04791/) (2022)
- [ARLO: A Framework for Automated Reinforcement Learning](https://mlsanity.com/paper/2205.10416/) (2022)

## Abstract (arXiv)

MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a comprehensive and flexible framework to minimize the effort in implementing and testing novel RL methodologies. Indeed, the architecture of MushroomRL is built in such a way that every component of an RL problem is already provided, and most of the time users can only focus on the implementation of their own algorithms and experiments. The result is a library from which RL researchers can significantly benefit in the critical phase of the empirical analysis of their works. MushroomRL stable code, tutorials and documentation can be found at https://github.com/MushroomRL/mushroom-rl.