Towards a Better Understanding of Representation Dynamics under TD-learning

• URL: http://arxiv.org/abs/2305.18491v1
• Date: Mon, 29 May 2023 13:34:40 GMT
• Title: Towards a Better Understanding of Representation Dynamics under TD-learning
• Authors: Yunhao Tang, R\'emi Munos
• Abstract summary: TD-learning is a foundation reinforcement learning (RL) algorithm for value prediction. In this work, we consider the question: how does end-to-end TD-learning impact the representation over time? We first show that when the environments are reversible, end-to-end TD-learning strictly decreases the value approximation error over time.
• Score: 23.65188248947536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: TD-learning is a foundation reinforcement learning (RL) algorithm for value prediction. Critical to the accuracy of value predictions is the quality of state representations. In this work, we consider the question: how does end-to-end TD-learning impact the representation over time? Complementary to prior work, we provide a set of analysis that sheds further light on the representation dynamics under TD-learning. We first show that when the environments are reversible, end-to-end TD-learning strictly decreases the value approximation error over time. Under further assumptions on the environments, we can connect the representation dynamics with spectral decomposition over the transition matrix. This latter finding establishes fitting multiple value functions from randomly generated rewards as a useful auxiliary task for representation learning, as we empirically validate on both tabular and Atari game suites.

Related papers

• What Makes Pre-Trained Visual Representations Successful for Robust Manipulation? [57.92924256181857]
  We find that visual representations designed for manipulation and control tasks do not necessarily generalize under subtle changes in lighting and scene texture. We find that emergent segmentation ability is a strong predictor of out-of-distribution generalization among ViT models.
  arXiv  Detail & Related papers  (2023-11-03T18:09:08Z)
• A Study of Forward-Forward Algorithm for Self-Supervised Learning [65.268245109828]
  We study the performance of forward-forward vs. backpropagation for self-supervised representation learning. Our main finding is that while the forward-forward algorithm performs comparably to backpropagation during (self-supervised) training, the transfer performance is significantly lagging behind in all the studied settings.
  arXiv  Detail & Related papers  (2023-09-21T10:14:53Z)
• Multi-Task Self-Supervised Time-Series Representation Learning [3.31490164885582]
  Time-series representation learning can extract representations from data with temporal dynamics and sparse labels. We propose a new time-series representation learning method by combining the advantages of self-supervised tasks. We evaluate the proposed framework on three downstream tasks: time-series classification, forecasting, and anomaly detection.
  arXiv  Detail & Related papers  (2023-03-02T07:44:06Z)
• 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)
• Palm up: Playing in the Latent Manifold for Unsupervised Pretraining [31.92145741769497]
  We propose an algorithm that exhibits an exploratory behavior whilst it utilizes large diverse datasets. Our key idea is to leverage deep generative models that are pretrained on static datasets and introduce a dynamic model in the latent space. We then employ an unsupervised reinforcement learning algorithm to explore in this environment and perform unsupervised representation learning on the collected data.
  arXiv  Detail & Related papers  (2022-10-19T22:26:12Z)
• Pretraining the Vision Transformer using self-supervised methods for vision based Deep Reinforcement Learning [0.0]
  We study pretraining a Vision Transformer using several state-of-the-art self-supervised methods and assess the quality of the learned representations. Our results show that all methods are effective in learning useful representations and avoiding representational collapse. The encoder pretrained with the temporal order verification task shows the best results across all experiments.
  arXiv  Detail & Related papers  (2022-09-22T10:18:59Z)
• Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representation [16.643709221279764]
  We propose a novel pretext task -temporal overlap rate (STOR) prediction. It stems from observation that humans are capable of discriminating overlap rates of videos in space and time. We employ a joint task combining contrastive learning to further the enhance-temporal representation learning.
  arXiv  Detail & Related papers  (2021-12-16T14:31:22Z)
• Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations [78.12377360145078]
  Contrastive self-supervised learning has outperformed supervised pretraining on many downstream tasks like segmentation and object detection. In this paper, we first study how biases in the dataset affect existing methods. We show that current contrastive approaches work surprisingly well across: (i) object- versus scene-centric, (ii) uniform versus long-tailed and (iii) general versus domain-specific datasets.
  arXiv  Detail & Related papers  (2021-06-10T17:59:13Z)
• Latent Representation Prediction Networks [0.0]
  We find this principle of learning representations unsatisfying. We propose a new way of jointly learning this representation along with the prediction function. Our approach is shown to be more sample-efficient than standard reinforcement learning methods.
  arXiv  Detail & Related papers  (2020-09-20T14:26:03Z)
• Predicting What You Already Know Helps: Provable Self-Supervised Learning [60.27658820909876]
  Self-supervised representation learning solves auxiliary prediction tasks (known as pretext tasks) without requiring labeled data. We show a mechanism exploiting the statistical connections between certain em reconstruction-based pretext tasks that guarantee to learn a good representation. We prove the linear layer yields small approximation error even for complex ground truth function class.
  arXiv  Detail & Related papers  (2020-08-03T17:56:13Z)
• 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.