On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning

• URL: http://arxiv.org/abs/2306.05637v1
• Date: Fri, 9 Jun 2023 02:47:21 GMT
• Title: On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning
• Authors: Hojoon Lee and Koanho Lee and Dongyoon Hwang and Hyunho Lee and Byungkun Lee and Jaegul Choo
• Abstract summary: unsupervised representation learning (URL) has improved the sample efficiency of Reinforcement Learning (RL) We propose a novel URL framework that causally predicts future states while increasing the dimension of the latent manifold. Our framework effectively learns predictive representations without collapse, which significantly improves the sample efficiency of state-of-the-art URL methods on the Atari 100k benchmark.
• Score: 23.876039876806182
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, unsupervised representation learning (URL) has improved the sample efficiency of Reinforcement Learning (RL) by pretraining a model from a large unlabeled dataset. The underlying principle of these methods is to learn temporally predictive representations by predicting future states in the latent space. However, an important challenge of this approach is the representational collapse, where the subspace of the latent representations collapses into a low-dimensional manifold. To address this issue, we propose a novel URL framework that causally predicts future states while increasing the dimension of the latent manifold by decorrelating the features in the latent space. Through extensive empirical studies, we demonstrate that our framework effectively learns predictive representations without collapse, which significantly improves the sample efficiency of state-of-the-art URL methods on the Atari 100k benchmark. The code is available at https://github.com/dojeon-ai/SimTPR.

Related papers

• ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting [7.637123047745445]
  Self-supervised methods are increasingly adapted to learn spatial-temporal representations. Current value reconstruction and future value prediction are integrated into the pre-training framework. Multi-time scale analysis is incorporated into the self-supervised loss to enhance predictive capability.
  arXiv  Detail & Related papers  (2024-12-19T05:33:55Z)
• USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation [24.90512145836643]
  We introduce a Unified Skeleton-based Dense Representation Learning framework based on feature decorrelation. We show that our approach significantly outperforms the current state-of-the-art (SOTA) approaches.
  arXiv  Detail & Related papers  (2024-12-12T12:20:27Z)
• Idempotent Unsupervised Representation Learning for Skeleton-Based Action Recognition [13.593511876719367]
  We propose a novel skeleton-based idempotent generative model (IGM) for unsupervised representation learning. Our experiments on benchmark datasets, NTU RGB+D and PKUMMD, demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2024-10-27T06:29:04Z)
• State Sequences Prediction via Fourier Transform for Representation Learning [111.82376793413746]
  We propose State Sequences Prediction via Fourier Transform (SPF), a novel method for learning expressive representations efficiently. We theoretically analyze the existence of structural information in state sequences, which is closely related to policy performance and signal regularity. Experiments demonstrate that the proposed method outperforms several state-of-the-art algorithms in terms of both sample efficiency and performance.
  arXiv  Detail & Related papers  (2023-10-24T14:47:02Z)
• Understanding Self-Predictive Learning for Reinforcement Learning [61.62067048348786]
  We study the learning dynamics of self-predictive learning for reinforcement learning. We propose a novel self-predictive algorithm that learns two representations simultaneously.
  arXiv  Detail & Related papers  (2022-12-06T20:43:37Z)
• Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning [105.70602423944148]
  We propose a novel method, called value-consistent representation learning (VCR), to learn representations that are directly related to decision-making. Instead of aligning this imagined state with a real state returned by the environment, VCR applies a $Q$-value head on both states and obtains two distributions of action values. It has been demonstrated that our methods achieve new state-of-the-art performance for search-free RL algorithms.
  arXiv  Detail & Related papers  (2022-06-25T03:02:25Z)
• Bayesian Graph Contrastive Learning [55.36652660268726]
  We propose a novel perspective of graph contrastive learning methods showing random augmentations leads to encoders. Our proposed method represents each node by a distribution in the latent space in contrast to existing techniques which embed each node to a deterministic vector. We show a considerable improvement in performance compared to existing state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-15T01:45:32Z)
• An Effective Baseline for Robustness to Distributional Shift [5.627346969563955]
  Refraining from confidently predicting when faced with categories of inputs different from those seen during training is an important requirement for the safe deployment of deep learning systems. We present a simple, but highly effective approach to deal with out-of-distribution detection that uses the principle of abstention.
  arXiv  Detail & Related papers  (2021-05-15T00:46:11Z)
• Relation-Guided Representation Learning [53.60351496449232]
  We propose a new representation learning method that explicitly models and leverages sample relations. Our framework well preserves the relations between samples. By seeking to embed samples into subspace, we show that our method can address the large-scale and out-of-sample problem.
  arXiv  Detail & Related papers  (2020-07-11T10:57:45Z)
• Value-driven Hindsight Modelling [68.658900923595]
  Value estimation is a critical component of the reinforcement learning (RL) paradigm. Model learning can make use of the rich transition structure present in sequences of observations, but this approach is usually not sensitive to the reward function. We develop an approach for representation learning in RL that sits in between these two extremes. This provides tractable prediction targets that are directly relevant for a task, and can thus accelerate learning the value function.
  arXiv  Detail & Related papers  (2020-02-19T18:10:20Z)

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.