Environment Agnostic Representation for Visual Reinforcement Learning

2023-01-01 · ICCV 2023 1 · Hyesong Choi, Hunsang Lee, Seongwon Jeong, Dongbo Min

Generalization capability of vision-based deep reinforcement learning (RL) is indispensable to deal with dynamic environment changes that exist in visual observations. The high-dimensional space of the visual input, however, imposes challenges in adapting an agent to unseen environments. In this work, we propose Environment Agnostic Reinforcement learning (EAR), which is a compact framework for domain generalization of the visual deep RL. Environment-agnostic features (EAFs) are extracted by leveraging three novel objectives based on feature factorization, reconstruction, and episode-aware state shifting, so that policy learning is accomplished only with vital features. EAR is a simple single-stage method with a low model complexity and a fast inference time, ensuring a high reproducibility, while attaining state-of-the-art performance in the DeepMind Control Suite and DrawerWorld benchmarks.

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Code (1)

doihye/ear 공식 구현 pytorch

Tasks

Deep Reinforcement LearningDomain Generalization reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Insights on Visual Representations for Embodied Navigation Tasks

2020-01-01 · ICLR 2020 1 · Erik Wijmans, Julian Straub, Irfan Essa, Dhruv Batra 외

Recent advances in deep reinforcement learning require a large amount of training data and generally result in representations that are often over specialized to the target task. In this work, we study the underlying pot…

Deep Reinforcement Learning Reinforcement Learning

CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations

2022-07-05 · Findings (NAACL) 2022 7 · Jialu Li, Hao Tan, Mohit Bansal

Vision-and-Language Navigation (VLN) tasks require an agent to navigate through the environment based on language instructions. In this paper, we aim to solve two key challenges in this task: utilizing multilingual instr…

Navigate Representation Learning Vision and Language Navigation Vision-Language Navigation

Learning to Navigate Using Mid-Level Visual Priors

2019-12-23 · Alexander Sax, Jeffrey O. Zhang, Bradley Emi, Amir Zamir 외

How much does having visual priors about the world (e.g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e.g. navigating a complex environment)? What are the consequences of not utili…

Navigate reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning

2022-02-17 · Che Wang, Xufang Luo, Keith Ross, Dongsheng Li

We propose VRL3, a powerful data-driven framework with a simple design for solving challenging visual deep reinforcement learning (DRL) tasks. We analyze a number of major obstacles in taking a data-driven approach, and …

Deep Reinforcement Learning Offline RL reinforcement-learning Reinforcement Learning+1

Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning

2021-08-06 · Iou-Jen Liu, Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing

Solving complex real-world tasks, e.g., autonomous fleet control, often involves a coordinated team of multiple agents which learn strategies from visual inputs via reinforcement learning. Many existing multi-agent reinf…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)