# Symmetry Learning for Function Approximation in Reinforcement Learning

arXiv: https://arxiv.org/abs/1706.02999 · HTML: https://mlsanity.com/paper/1706.02999/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2017.

## Built on by (most cited first, 14 of 14)

- [Continuous MDP Homomorphisms and Homomorphic Policy Gradient](https://mlsanity.com/paper/2209.07364/) (2022)
- [Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2106.00136/) (2021)
- [Generalization in Cooperative Multi-Agent Systems](https://mlsanity.com/paper/2202.00104/) (2022)
- [Structure in Deep Reinforcement Learning: A Survey and Open Problems](https://mlsanity.com/paper/2306.16021/) (2023)
- [Policy Gradient Methods in the Presence of Symmetries and State Abstractions](https://mlsanity.com/paper/2305.05666/) (2023)
- [Reinforcement Learning in Factored Action Spaces using Tensor Decompositions](https://mlsanity.com/paper/2110.14538/) (2021)
- [Effects of Spectral Normalization in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2212.05331/) (2022)
- [Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction](https://mlsanity.com/paper/2407.13364/) (2024)
- [Addressing imperfect symmetry: A novel symmetry-learning actor-critic extension](https://mlsanity.com/paper/2309.02711/) (2023)
- [Symmetric Linear Bandits with Hidden Symmetry](https://mlsanity.com/paper/2405.13899/) (2024)
- [Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning](https://mlsanity.com/paper/2405.19414/) (2024)
- [Deep deterministic policy gradient with symmetric data augmentation for lateral attitude tracking control of a fixed-wing aircraft](https://mlsanity.com/paper/2407.11077/) (2024)
- [More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences](https://mlsanity.com/paper/2110.10632/) (2021)
- [Sample-Efficient Reinforcement Learning with Symmetry-Guided Demonstrations for Robotic Manipulation](https://mlsanity.com/paper/2304.06055/) (2023)

## Abstract (arXiv)

In this paper we explore methods to exploit symmetries for ensuring sample efficiency in reinforcement learning (RL), this problem deserves ever increasing attention with the recent advances in the use of deep networks for complex RL tasks which require large amount of training data. We introduce a novel method to detect symmetries using reward trails observed during episodic experience and prove its completeness. We also provide a framework to incorporate the discovered symmetries for functional approximation. Finally we show that the use of potential based reward shaping is especially effective for our symmetry exploitation mechanism. Experiments on various classical problems show that our method improves the learning performance significantly by utilizing symmetry information.