Papers › SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement Learning

SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement Learning

21 Jun 2024arXiv:2406.15025archive 2025-07-28

Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara

An open challenge in reinforcement learning (RL) is the effective deployment of a trained policy to new or slightly different situations as well as semantically-similar environments. We introduce Symmetry-Invariant Transformer (SiT), a scalable vision transformer (ViT) that leverages both local and global data patterns in a self-supervised manner to improve generalisation. Central to our approach is Graph Symmetric Attention, which refines the traditional self-attention mechanism to preserve graph symmetries, resulting in invariant and equivariant latent representations. We showcase SiT's superior generalization over ViTs on MiniGrid and Procgen RL benchmarks, and its sample efficiency on Atari 100k and CIFAR10.

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Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer Vision Transformer

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