Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents

2018-10-20 · Yosuke Fukuchi, Masahiko Osawa, Hiroshi Yamakawa, Michita Imai

In cooperation, the workers must know how co-workers behave. However, an agent's policy, which is embedded in a statistical machine learning model, is hard to understand, and requires much time and knowledge to comprehend. Therefore, it is difficult for people to predict the behavior of machine learning robots, which makes Human Robot Cooperation challenging. In this paper, we propose Instruction-based Behavior Explanation (IBE), a method to explain an autonomous agent's future behavior. In IBE, an agent can autonomously acquire the expressions to explain its own behavior by reusing the instructions given by a human expert to accelerate the learning of the agent's policy. IBE also enables a developmental agent, whose policy may change during the cooperation, to explain its own behavior with sufficient time granularity.

📄 PDF Abstract BibTeX arXiv:1810.08811

Code (0)

등록된 구현이 없습니다.

Tasks

BIG-bench Machine Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents

2023-01-24 · Yotam Amitai, Guy Avni, Ofra Amir

As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial. Most explainable reinforcement learning (XRL) methods generate a static explanation d…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving

2021-02-23 · Chen Tang, Nishan Srishankar, Sujitha Martin, Masayoshi Tomizuka

Explainability is essential for autonomous vehicles and other robotics systems interacting with humans and other objects during operation. Humans need to understand and anticipate the actions taken by the machines for tr…

Autonomous Driving Autonomous Vehicles

Self-CTRL: Self-Consistency Training with Reinforcement Learning

2026-06-16 · Itamar Pres, Laura Ruis, Melat Ghebreselassie, Belinda Z. Li 외 arxiv

Language models (LMs) that faithfully describe their own behavior can more easily be audited, understood, and trusted by users. This paper describes Self-Consistency Training with Reinforcement Learning (Self-CTRL), a me…

Reinforcement Learning

Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving

2026-05-11 · Aron Distelzweig, Faris Janjoš, Andreas Look, Anna Rothenhäusler 외 arxiv

Recent Autonomous Driving (AD) works such as GigaFlow and PufferDrive have unlocked Reinforcement Learning (RL) at scale as a training strategy for driving policies. Yet such policies remain disconnected from established…

Reinforcement Learning Autonomous Driving

Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning

2024-02-21 · Liu Weiwei, Hu Wenxuan, Jing Wei, Lei Lanxin 외

Autonomous vehicles trained through Multi-Agent Reinforcement Learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with di…

Autonomous Driving Autonomous Vehicles Multi-agent Reinforcement Learning