# ChainerRL: A Deep Reinforcement Learning Library

arXiv: https://arxiv.org/abs/1912.03905 · HTML: https://mlsanity.com/paper/1912.03905/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

## Built on by (most cited first, 30 of 51)

- [Tianshou: a Highly Modularized Deep Reinforcement Learning Library](https://mlsanity.com/paper/2107.14171/) (2021)
- [CORL: Research-oriented Deep Offline Reinforcement Learning Library](https://mlsanity.com/paper/2210.07105/) (2022)
- [d3rlpy: An Offline Deep Reinforcement Learning Library](https://mlsanity.com/paper/2111.03788/) (2021)
- [GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving](https://mlsanity.com/paper/2111.08575/) (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)
- [The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors](https://mlsanity.com/paper/2101.11071/) (2021)
- [BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence](https://mlsanity.com/paper/2402.12174/) (2024)
- [Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance](https://mlsanity.com/paper/2206.03787/) (2022)
- [Recurrent Off-policy Baselines for Memory-based Continuous Control](https://mlsanity.com/paper/2110.12628/) (2021)
- [Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization](https://mlsanity.com/paper/2102.11055/) (2021)
- [Topological Experience Replay](https://mlsanity.com/paper/2203.15845/) (2022)
- [Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning](https://mlsanity.com/paper/2402.03046/) (2024)
- [A2C is a special case of PPO](https://mlsanity.com/paper/2205.09123/) (2022)
- [Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning](https://mlsanity.com/paper/2103.17258/) (2021)
- [PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems](https://mlsanity.com/paper/2109.03970/) (2021)
- [Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization](https://mlsanity.com/paper/2110.04686/) (2021)
- [Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks](https://mlsanity.com/paper/2302.05326/) (2023)
- [Investigating the Histogram Loss in Regression](https://mlsanity.com/paper/2402.13425/) (2024)
- [Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey](https://mlsanity.com/paper/2411.05614/) (2024)
- [A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems](https://mlsanity.com/paper/2109.12073/) (2021)
- [ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives](https://mlsanity.com/paper/2112.04123/) (2021)
- [XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library](https://mlsanity.com/paper/2312.16248/) (2023)
- [Dataset Distillation for Offline Reinforcement Learning](https://mlsanity.com/paper/2407.20299/) (2024)
- [RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation](https://mlsanity.com/paper/2206.02544/) (2022)
- [RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents](https://mlsanity.com/paper/2208.06448/) (2022)
- [RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control](https://mlsanity.com/paper/2306.03530/) (2023)
- [CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms](https://mlsanity.com/paper/2111.08819/) (2021)
- [RLLTE: Long-Term Evolution Project of Reinforcement Learning](https://mlsanity.com/paper/2309.16382/) (2023)
- [Evaluation of Reinforcement Learning for Autonomous Penetration Testing using A3C, Q-learning and DQN](https://mlsanity.com/paper/2407.15656/) (2024)

## Abstract (arXiv)

In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning framework. ChainerRL implements a comprehensive set of DRL algorithms and techniques drawn from state-of-the-art research in the field. To foster reproducible research, and for instructional purposes, ChainerRL provides scripts that closely replicate the original papers' experimental settings and reproduce published benchmark results for several algorithms. Lastly, ChainerRL offers a visualization tool that enables the qualitative inspection of trained agents. The ChainerRL source code can be found on GitHub: https://github.com/chainer/chainerrl.