# Learning Invariances for Policy Generalization

arXiv: https://arxiv.org/abs/1809.02591 · HTML: https://mlsanity.com/paper/1809.02591/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [A Survey of Zero-shot Generalisation in Deep Reinforcement Learning](https://mlsanity.com/paper/2111.09794/) (2021)
- [Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning](https://mlsanity.com/paper/2101.05265/) (2021)
- [Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions](https://mlsanity.com/paper/2111.14629/) (2021)
- [Model-based Lifelong Reinforcement Learning with Bayesian Exploration](https://mlsanity.com/paper/2210.11579/) (2022)
- [Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation](https://mlsanity.com/paper/2201.12300/) (2022)
- [Skill-aware Mutual Information Optimisation for Generalisation in Reinforcement Learning](https://mlsanity.com/paper/2406.04815/) (2024)
- [Policy-Induced Self-Supervision Improves Representation Finetuning in Visual RL](https://mlsanity.com/paper/2302.06009/) (2023)

## Abstract (arXiv)

While recent progress has spawned very powerful machine learning systems, those agents remain extremely specialized and fail to transfer the knowledge they gain to similar yet unseen tasks. In this paper, we study a simple reinforcement learning problem and focus on learning policies that encode the proper invariances for generalization to different settings. We evaluate three potential methods for policy generalization: data augmentation, meta-learning and adversarial training. We find our data augmentation method to be effective, and study the potential of meta-learning and adversarial learning as alternative task-agnostic approaches.