Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning

• URL: http://arxiv.org/abs/2404.02235v1
• Date: Tue, 2 Apr 2024 18:45:01 GMT
• Title: Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning
• Authors: Jonathan C. Balloch, Rishav Bhagat, Geigh Zollicoffer, Ruoran Jia, Julia Kim, Mark O. Riedl,
• Abstract summary: We test eleven popular exploration algorithms on a variety of transfer types -- or novelties'' -- to identify the characteristics that positively affect online transfer learning. Our analysis shows that some characteristics correlate with improved performance and efficiency across a wide range of transfer tasks, while others only improve transfer performance with respect to specific environment changes.
• Score: 13.322146980081778
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In deep reinforcement learning (RL) research, there has been a concerted effort to design more efficient and productive exploration methods while solving sparse-reward problems. These exploration methods often share common principles (e.g., improving diversity) and implementation details (e.g., intrinsic reward). Prior work found that non-stationary Markov decision processes (MDPs) require exploration to efficiently adapt to changes in the environment with online transfer learning. However, the relationship between specific exploration characteristics and effective transfer learning in deep RL has not been characterized. In this work, we seek to understand the relationships between salient exploration characteristics and improved performance and efficiency in transfer learning. We test eleven popular exploration algorithms on a variety of transfer types -- or ``novelties'' -- to identify the characteristics that positively affect online transfer learning. Our analysis shows that some characteristics correlate with improved performance and efficiency across a wide range of transfer tasks, while others only improve transfer performance with respect to specific environment changes. From our analysis, make recommendations about which exploration algorithm characteristics are best suited to specific transfer situations.

Related papers

• Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learning [3.3148826359547523]
  We develop a semi-supervised alignment loss to match different spaces with a set of encoder-decoders. In comparison to prior works, our method does not require data to be aligned, paired or collected by expert policies.
  arXiv  Detail & Related papers  (2023-12-05T19:26:01Z)
• Evaluating the structure of cognitive tasks with transfer learning [67.22168759751541]
  This study investigates the transferability of deep learning representations between different EEG decoding tasks. We conduct extensive experiments using state-of-the-art decoding models on two recently released EEG datasets.
  arXiv  Detail & Related papers  (2023-07-28T14:51:09Z)
• Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes [61.22448274621503]
  In real-world applications, tasks often exhibit partial similarity, where certain aspects are similar while others are different or irrelevant. Our study explores various types of transfer learning, encompassing two options for parameter transfer. We provide practical guidelines for determining the number of features in the common and task-specific parts for improved generalization performance.
  arXiv  Detail & Related papers  (2023-06-08T03:08:40Z)
• Feasibility of Transfer Learning: A Mathematical Framework [4.530876736231948]
  It begins by establishing the necessary mathematical concepts and constructing a mathematical framework for transfer learning. It then identifies and formulates the three-step transfer learning procedure as an optimization problem, allowing for the resolution of the feasibility issue.
  arXiv  Detail & Related papers  (2023-05-22T12:44:38Z)
• The Role of Exploration for Task Transfer in Reinforcement Learning [8.817381809671804]
  We re-examine the exploration--exploitation trade-off in the context of transfer learning. In this work, we review reinforcement learning exploration methods, define a taxonomy with which to organize them, analyze these methods' differences in the context of task transfer, and suggest avenues for future investigation.
  arXiv  Detail & Related papers  (2022-10-11T01:23:21Z)
• ALLSH: Active Learning Guided by Local Sensitivity and Hardness [98.61023158378407]
  We propose to retrieve unlabeled samples with a local sensitivity and hardness-aware acquisition function. Our method achieves consistent gains over the commonly used active learning strategies in various classification tasks.
  arXiv  Detail & Related papers  (2022-05-10T15:39:11Z)
• TRAIL: Near-Optimal Imitation Learning with Suboptimal Data [100.83688818427915]
  We present training objectives that use offline datasets to learn a factored transition model. Our theoretical analysis shows that the learned latent action space can boost the sample-efficiency of downstream imitation learning. To learn the latent action space in practice, we propose TRAIL (Transition-Reparametrized Actions for Imitation Learning), an algorithm that learns an energy-based transition model.
  arXiv  Detail & Related papers  (2021-10-27T21:05:00Z)
• Uniform Priors for Data-Efficient Transfer [65.086680950871]
  We show that features that are most transferable have high uniformity in the embedding space. We evaluate the regularization on its ability to facilitate adaptation to unseen tasks and data.
  arXiv  Detail & Related papers  (2020-06-30T04:39:36Z)
• Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach [55.83558520598304]
  We propose a brand new solution to reuse experiences and transfer value functions among multiple students via model distillation. We also describe how to design an efficient communication protocol to exploit heterogeneous knowledge. Our proposed framework, namely Learning and Teaching Categorical Reinforcement, shows promising performance on stabilizing and accelerating learning progress.
  arXiv  Detail & Related papers  (2020-02-06T11:31:04Z)
• Inter- and Intra-domain Knowledge Transfer for Related Tasks in Deep Character Recognition [2.320417845168326]
  Pre-training a deep neural network on the ImageNet dataset is a common practice for training deep learning models. The technique of pre-training on one task and then retraining on a new one is called transfer learning. In this paper we analyse the effectiveness of using deep transfer learning for character recognition tasks.
  arXiv  Detail & Related papers  (2020-01-02T14:18:25Z)

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.