# SaLinA: Sequential Learning of Agents

arXiv: https://arxiv.org/abs/2110.07910 · HTML: https://mlsanity.com/paper/2110.07910/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 10 references that are in the map.
- First posted on arXiv 15 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [Acme: A Research Framework for Distributed Reinforcement Learning](https://mlsanity.com/paper/2006.00979/) (2020)

## Built on by (most cited first, 6 of 6)

- [d3rlpy: An Offline Deep Reinforcement Learning Library](https://mlsanity.com/paper/2111.03788/) (2021)
- [Building a Subspace of Policies for Scalable Continual Learning](https://mlsanity.com/paper/2211.10445/) (2022)
- [Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching](https://mlsanity.com/paper/2110.14457/) (2021)
- [Beyond Supervised Continual Learning: a Review](https://mlsanity.com/paper/2208.14307/) (2022)
- [Avalanche RL: a Continual Reinforcement Learning Library](https://mlsanity.com/paper/2202.13657/) (2022)
- [Group-Agent Reinforcement Learning](https://mlsanity.com/paper/2202.05135/) (2022)

## Abstract (arXiv)

SaLinA is a simple library that makes implementing complex sequential learning models easy, including reinforcement learning algorithms. It is built as an extension of PyTorch: algorithms coded with SALINA can be understood in few minutes by PyTorch users and modified easily. Moreover, SaLinA naturally works with multiple CPUs and GPUs at train and test time, thus being a good fit for the large-scale training use cases. In comparison to existing RL libraries, SaLinA has a very low adoption cost and capture a large variety of settings (model-based RL, batch RL, hierarchical RL, multi-agent RL, etc.). But SaLinA does not only target RL practitioners, it aims at providing sequential learning capabilities to any deep learning programmer.