Data to 7 Oct 2026

MushroomRL: Simplifying Reinforcement Learning Research

arXiv 2001.01102 ↗First posted 4 Jan 2020cs.LG stat.MLcited by 50

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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.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 9; 2023: 15; 2024: 8; 2025: 5; 2026: 2.

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2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 139
2021d3rlpy: An Offline Deep Reinforcement Learning Librarycited by 84
2023TorchRL: A data-driven decision-making library for PyTorchcited by 59
2023Multi-Task Reinforcement Learning with Mixture of Orthogonal Expertscited by 45
2024Sharing Knowledge in Multi-Task Deep Reinforcement Learningcited by 42
2022skrl: Modular and Flexible Library for Reinforcement Learningcited by 35
2023LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotioncited by 31
2023LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learningcited by 28
2021Adaptive Rational Activations to Boost Deep Reinforcement Learningcited by 23
2022Robot Learning of Mobile Manipulation with Reachability Behavior Priorscited by 20
2021A Survey on Deep Reinforcement Learning for Audio-Based Applicationscited by 17
2021Value Iteration in Continuous Actions, States and Timecited by 17

38 more

2022Boosting Object Representation Learning via Motion and Object Continuitycited by 17
2024Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learningcited by 17
2021Composable Energy Policies for Reactive Motion Generation and Reinforcement Learningcited by 16
2022A2C is a special case of PPOcited by 16
2022Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interactioncited by 15
2023Robust Adversarial Reinforcement Learning via Bounded Rationality Curriculacited by 9
2021Robust Value Iteration for Continuous Control Taskscited by 8
2023Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policiescited by 8
2021Continuous-Time Fitted Value Iteration for Robust Policiescited by 6
2024Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learningcited by 6
2022Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discoverycited by 5
2022Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworkscited by 5
2023XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Librarycited by 5
2023RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Controlcited by 4
2021CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithmscited by 3
2022Dimensionality Reduction and Prioritized Exploration for Policy Searchcited by 3
2023AdaStop: adaptive statistical testing for sound comparisons of Deep RL agentscited by 3
2023RLLTE: Long-Term Evolution Project of Reinforcement Learningcited by 3
2023Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learningcited by 3
2022ARLO: A Framework for Automated Reinforcement Learningcited by 2
2023Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbingcited by 2
2024Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learningcited by 2
2025Sparsity-Driven Plasticity in Multi-Task Reinforcement Learningcited by 2
2021Sim-Env: Decoupling OpenAI Gym Environments from Simulation Modelscited by 1
2023RFRL Gym: A Reinforcement Learning Testbed for Cognitive Radio Applicationscited by 1
2024On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learningcited by 1
2024Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Reviewcited by 1
2021The AI Arena: A Framework for Distributed Multi-Agent Reinforcement Learningcited by 0
2023Dependency Structure Search Bayesian Optimization for Decision Making Modelscited by 0
2023EduGym: An Environment and Notebook Suite for Reinforcement Learning Educationcited by 0
2024Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learningcited by 0
2024Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learningcited by 0
2025Dynamic Obstacle Avoidance with Bounded Rationality Adversarial Reinforcement Learningcited by 0
2025Ensemble Elastic DQN: A Step Dependent Ensemble Approach for Reducing Overestimation in Deep Value-Based Reinforcement Learningcited by 0
2025ObjectRL: An Object-Oriented Reinforcement Learning Codebasecited by 0
2025elsciRL: Integrating Language Solutions into Reinforcement Learning Problem Settingscited by 0
2026RLLBC-Lib: An Educational Code Library for Reinforcement Learning and Learning-Based Controlcited by 0
2026Robust Adversarial Reinforcement Learning with Risk Sensitivity and Critic Consistency Regularizationcited by 0