On The Transferability of Deep-Q Networks

• URL: http://arxiv.org/abs/2110.02639v1
• Date: Wed, 6 Oct 2021 10:29:37 GMT
• Title: On The Transferability of Deep-Q Networks
• Authors: Matthia Sabatelli, Pierre Geurts
• Abstract summary: Transfer Learning is an efficient machine learning paradigm that allows overcoming some of the hurdles that characterize the successful training of deep neural networks. While exploiting TL is a well established and successful training practice in Supervised Learning (SL), its applicability in Deep Reinforcement Learning (DRL) is rarer. In this paper, we study the level of transferability of three different variants of Deep-Q Networks on popular DRL benchmarks and on a set of novel, carefully designed control tasks.
• Score: 6.822707222147354
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transfer Learning (TL) is an efficient machine learning paradigm that allows overcoming some of the hurdles that characterize the successful training of deep neural networks, ranging from long training times to the needs of large datasets. While exploiting TL is a well established and successful training practice in Supervised Learning (SL), its applicability in Deep Reinforcement Learning (DRL) is rarer. In this paper, we study the level of transferability of three different variants of Deep-Q Networks on popular DRL benchmarks as well as on a set of novel, carefully designed control tasks. Our results show that transferring neural networks in a DRL context can be particularly challenging and is a process which in most cases results in negative transfer. In the attempt of understanding why Deep-Q Networks transfer so poorly, we gain novel insights into the training dynamics that characterizes this family of algorithms.

Related papers

• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Deep Fusion: Efficient Network Training via Pre-trained Initializations [3.9146761527401424]
  We present Deep Fusion, an efficient approach to network training that leverages pre-trained initializations of smaller networks. Our experiments show how Deep Fusion is a practical and effective approach that not only accelerates the training process but also reduces computational requirements. We validate our theoretical framework, which guides the optimal use of Deep Fusion, showing that it significantly reduces both training time and resource consumption.
  arXiv  Detail & Related papers  (2023-06-20T21:30:54Z)
• Solving Large-scale Spatial Problems with Convolutional Neural Networks [88.31876586547848]
  We employ transfer learning to improve training efficiency for large-scale spatial problems. We propose that a convolutional neural network (CNN) can be trained on small windows of signals, but evaluated on arbitrarily large signals with little to no performance degradation.
  arXiv  Detail & Related papers  (2023-06-14T01:24:42Z)
• Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks [49.808194368781095]
  We show that three-layer neural networks have provably richer feature learning capabilities than two-layer networks. This work makes progress towards understanding the provable benefit of three-layer neural networks over two-layer networks in the feature learning regime.
  arXiv  Detail & Related papers  (2023-05-11T17:19:30Z)
• The State of Sparse Training in Deep Reinforcement Learning [23.034856834801346]
  The use of sparse neural networks has seen rapid growth in recent years, particularly in computer vision. Their appeal stems largely from the reduced number of parameters required to train and store, as well as an increase in learning efficiency. We perform a systematic investigation into applying a number of existing sparse training techniques on a variety of Deep Reinforcement Learning agents and environments.
  arXiv  Detail & Related papers  (2022-06-17T14:08:00Z)
• Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning [5.130440339897478]
  Bringing deep neural networks (DNNs) into safety critical applications requires a thorough treatment of the model's uncertainties. In this article, we survey methods that we developed to teach DNNs to be uncertain when they encounter new object classes. We also present training methods to learn from only a few labels with help of uncertainty quantification.
  arXiv  Detail & Related papers  (2022-05-30T08:31:03Z)
• Deep Reinforcement Learning with Spiking Q-learning [51.386945803485084]
  spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption. It provides a promising energy-efficient way for realistic control tasks by combining SNNs with deep reinforcement learning (RL)
  arXiv  Detail & Related papers  (2022-01-21T16:42:11Z)
• Provable Regret Bounds for Deep Online Learning and Control [77.77295247296041]
  We show that any loss functions can be adapted to optimize the parameters of a neural network such that it competes with the best net in hindsight. As an application of these results in the online setting, we obtain provable bounds for online control controllers.
  arXiv  Detail & Related papers  (2021-10-15T02:13:48Z)
• Training Larger Networks for Deep Reinforcement Learning [18.193180866998333]
  We show that naively increasing network capacity does not improve performance. We propose a novel method that consists of 1) wider networks with DenseNet connection, 2) decoupling representation learning from training of RL, and 3) a distributed training method to mitigate overfitting problems. Using this three-fold technique, we show that we can train very large networks that result in significant performance gains.
  arXiv  Detail & Related papers  (2021-02-16T02:16:54Z)
• Deep Transfer Learning with Ridge Regression [7.843067454030999]
  Deep models trained with massive amounts of data demonstrate promising generalisation ability on unseen data from relevant domains. We address this issue by leveraging the low-rank property of learnt feature vectors produced from deep neural networks (DNNs) with the closed-form solution provided in kernel ridge regression (KRR) Our method is successful on supervised and semi-supervised transfer learning tasks.
  arXiv  Detail & Related papers  (2020-06-11T20:21:35Z)
• Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment [84.57874289554839]
  Training deep neural networks on large-scale datasets requires significant hardware resources. Backpropagation, the workhorse for training these networks, is an inherently sequential process that is difficult to parallelize. We propose a neuro-biologically-plausible alternative to backprop that can be used to train deep networks.
  arXiv  Detail & Related papers  (2020-02-10T16:20:02Z)

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.