R3L: Relative Representations for Reinforcement Learning

• URL: http://arxiv.org/abs/2404.12917v3
• Date: Tue, 18 Feb 2025 15:17:38 GMT
• Title: R3L: Relative Representations for Reinforcement Learning
• Authors: Antonio Pio Ricciardi, Valentino Maiorca, Luca Moschella, Riccardo Marin, Emanuele Rodolà,
• Abstract summary: It is known that variations in input domains (e.g., different panorama colors due to seasonal changes) can disrupt agent performance. Recent advancements in the field of representation learning have demonstrated the possibility of combining components to create new models. We adapt this framework to the Visual Reinforcement Learning setting, allowing to combine agents components to create new agents capable of effectively handling novel visual-task pairs.
• Score: 17.76990521486307
• License:
• Abstract: Visual Reinforcement Learning is a popular and powerful framework that takes full advantage of the Deep Learning breakthrough. It is known that variations in input domains (e.g., different panorama colors due to seasonal changes) or task domains (e.g., altering the target speed of a car) can disrupt agent performance, necessitating new training for each variation. Recent advancements in the field of representation learning have demonstrated the possibility of combining components from different neural networks to create new models in a zero-shot fashion. In this paper, we build upon relative representations, a framework that maps encoder embeddings to a universal space. We adapt this framework to the Visual Reinforcement Learning setting, allowing to combine agents components to create new agents capable of effectively handling novel visual-task pairs not encountered during training. Our findings highlight the potential for model reuse, significantly reducing the need for retraining and, consequently, the time and computational resources required.

Related papers

• Towards Scalable and Versatile Weight Space Learning [51.78426981947659]
  This paper introduces the SANE approach to weight-space learning. Our method extends the idea of hyper-representations towards sequential processing of subsets of neural network weights.
  arXiv  Detail & Related papers  (2024-06-14T13:12:07Z)
• Reusable Architecture Growth for Continual Stereo Matching [92.36221737921274]
  We introduce a Reusable Architecture Growth (RAG) framework to learn new scenes continually in both supervised and self-supervised manners. RAG can maintain high reusability during growth by reusing previous units while obtaining good performance. We also present a Scene Router module to adaptively select the scene-specific architecture path at inference.
  arXiv  Detail & Related papers  (2024-03-30T13:24:58Z)
• Look-Ahead Selective Plasticity for Continual Learning of Visual Tasks [9.82510084910641]
  We propose a new mechanism that takes place during task boundaries, i.e., when one task finishes and another starts. We evaluate the proposed methods on benchmark computer vision datasets including CIFAR10 and TinyImagenet.
  arXiv  Detail & Related papers  (2023-11-02T22:00:23Z)
• RLFlow: Optimising Neural Network Subgraph Transformation with World Models [0.0]
  We propose a model-based agent which learns to optimise the architecture of neural networks by performing a sequence of subgraph transformations to reduce model runtime. We show our approach can match the performance of state of the art on common convolutional networks and outperform those by up to 5% on transformer-style architectures.
  arXiv  Detail & Related papers  (2022-05-03T11:52:54Z)
• Meta-Learning with Variational Bayes [0.0]
  We introduce a new approach to address the more general problem of generative meta-learning. Our contribution leverages the AEVB framework and mean-field variational Bayes, and creates fast-adapting latent-space generative models. At the heart of our contribution is a new result, showing that for a broad class of deep generative latent variable models, the relevant VB updates do not depend on any generative neural network.
  arXiv  Detail & Related papers  (2021-03-03T09:02:01Z)
• Essentials for Class Incremental Learning [43.306374557919646]
  Class-incremental learning results on CIFAR-100 and ImageNet improve over the state-of-the-art by a large margin, while keeping the approach simple.
  arXiv  Detail & Related papers  (2021-02-18T18:01:06Z)
• Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks [79.28094304325116]
  Graph Convolutional Networks are among the most promising approaches for capturing relationships among structured data points. We propose three novel self-supervised auxiliary tasks to train graph-based neural network models in a multi-task fashion.
  arXiv  Detail & Related papers  (2020-11-14T11:09:51Z)
• Reward Propagation Using Graph Convolutional Networks [61.32891095232801]
  We propose a new framework for learning potential functions by leveraging ideas from graph representation learning. Our approach relies on Graph Convolutional Networks which we use as a key ingredient in combination with the probabilistic inference view of reinforcement learning.
  arXiv  Detail & Related papers  (2020-10-06T04:38:16Z)
• Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory [79.42778415729475]
  We propose a novel incrementally trained recurrent architecture targeting explicitly multi-scale learning. We show how to extend the architecture of a simple RNN by separating its hidden state into different modules. We discuss a training algorithm where new modules are iteratively added to the model to learn progressively longer dependencies.
  arXiv  Detail & Related papers  (2020-06-29T08:35:49Z)
• Towards Backward-Compatible Representation Learning [86.39292571306395]
  We propose a way to learn visual features that are compatible with previously computed ones even when they have different dimensions. This enables visual search systems to bypass computing new features for all previously seen images when updating the embedding models. We propose a framework to train embedding models, called backward-compatible training (BCT), as a first step towards backward compatible representation learning.
  arXiv  Detail & Related papers  (2020-03-26T14:34:09Z)
• Efficient Learning of Model Weights via Changing Features During Training [0.0]
  We propose a machine learning model, which dynamically changes the features during training. Our main motivation is to update the model in a small content during the training process with replacing less descriptive features to new ones from a large pool.
  arXiv  Detail & Related papers  (2020-02-21T12:38:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.