Learning to Explore by Reinforcement over High-Level Options

2021-11-02 · Liu Juncheng, McCane Brendan, Mills Steven

Autonomous 3D environment exploration is a fundamental task for various applications such as navigation. The goal of exploration is to investigate a new environment and build its occupancy map efficiently. In this paper, we propose a new method which grants an agent two intertwined options of behaviors: "look-around" and "frontier navigation". This is implemented by an option-critic architecture and trained by reinforcement learning algorithms. In each timestep, an agent produces an option and a corresponding action according to the policy. We also take advantage of macro-actions by incorporating classic path-planning techniques to increase training efficiency. We demonstrate the effectiveness of the proposed method on two publicly available 3D environment datasets and the results show our method achieves higher coverage than competing techniques with better efficiency.

📄 PDF Abstract BibTeX arXiv:2111.01364

Code (0)

등록된 구현이 없습니다.

Tasks

Vocal Bursts Intensity Prediction

Multi-agent Deep Covering Skill Discovery

2022-10-07 · Jiayu Chen, Marina Haliem, Tian Lan, Vaneet Aggarwal

The use of skills (a.k.a., options) can greatly accelerate exploration in reinforcement learning, especially when only sparse reward signals are available. While option discovery methods have been proposed for individual…

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

A Study of Value-Aware Eigenoptions

2025-07-12 · Harshil Kotamreddy, Marlos C. Machado arxiv

Options, which impose an inductive bias toward temporal and hierarchical structure, offer a powerful framework for reinforcement learning (RL). While effective in sequential decision-making, they are often handcrafted ra…

Reinforcement Learning

Deep Learning of Intrinsically Motivated Options in the Arcade Learning Environment

2021-09-29 · Louis Bagot, Kevin Mets, Tom De Schepper, Peter Hellinckx 외

Although Intrinsic Motivation allows a Reinforcement Learning agent to generate directed behaviors in an environment, even with sparse or noisy rewards, combining intrinsic and extrinsic rewards is non trivial. As an alt…

Atari Games Benchmarking Deep Reinforcement Learning Fairness+2

Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning

2022-06-12 · Kushal Chauhan, Soumya Chatterjee, Akash Reddy, Balaraman Ravindran 외

The options framework in Hierarchical Reinforcement Learning breaks down overall goals into a combination of options or simpler tasks and associated policies, allowing for abstraction in the action space. Ideally, these …

Continual Learning Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning+2

Multi-Level Discovery of Deep Options

2017-03-24 · Roy Fox, Sanjay Krishnan, Ion Stoica, Ken Goldberg

Augmenting an agent's control with useful higher-level behaviors called options can greatly reduce the sample complexity of reinforcement learning, but manually designing options is infeasible in high-dimensional and abs…