1A1-S-061 Acquisition of Escape Action Sequence of Autonomous Outdoor Wheel Robot Using Reinforcement Learning : Learning Under Limited Sensor Information(Evolution and Learning for Robotics 1,Mega-Integration in Robotics and Mechatronics to Assist Our Daily Lives)

2005 ◽  
Vol 2005 (0) ◽  
pp. 45
Author(s):  
Yusuke KON ◽  
Sadayoshi MIKAMI
Keyword(s):  
Reinforcement Learning ◽  
Daily Lives ◽  
Action Sequence ◽  
Sensor Information

Reinforcement Learning in Latent Action Sequence Space

2020 ◽  
Author(s):  
Heecheol Kim ◽  
Masanori Yamada ◽  
Kosuke Miyoshi ◽  
Tomoharu Iwata ◽  
Hiroshi Yamakawa
Keyword(s):  
Reinforcement Learning ◽  
Sequence Space ◽  
Action Sequence

scholarly journals A Multi-Agent Control Architecture for a Robotic Wheelchair

2006 ◽  
Vol 3 (3) ◽  
pp. 179-189 ◽  
Author(s):  
C. Galindo ◽  
A. Cruz-Martin ◽  
J. L. Blanco ◽  
J. A. Fernńndez-Madrigal ◽  
J. Gonzalez
Keyword(s):  
Reinforcement Learning ◽  
Control Architecture ◽  
Daily Lives ◽  
Multi Agent ◽  
Human Skills ◽  
Overall Performance ◽  
Agent Control ◽  
Robotic Wheelchairs ◽  
Assistant Robots ◽  
Robotic Wheelchair

Assistant robots like robotic wheelchairs can perform an effective and valuable work in our daily lives. However, they eventually may need external help from humans in the robot environment (particularly, the driver in the case of a wheelchair) to accomplish safely and efficiently some tricky tasks for the current technology, i.e. opening a locked door, traversing a crowded area, etc. This article proposes a control architecture for assistant robots designed under a multi-agent perspective that facilitates the participation of humans into the robotic system and improves the overall performance of the robot as well as its dependability. Within our design, agents have their own intentions and beliefs, have different abilities (that include algorithmic behaviours and human skills) and also learn autonomously the most convenient method to carry out their actions through reinforcement learning. The proposed architecture is illustrated with a real assistant robot: a robotic wheelchair that provides mobility to impaired or elderly people.

scholarly journals A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1222
Author(s):  
Fanghui Huang ◽  
Yu Zhang ◽  
Ziqing Wang ◽  
Xinyang Deng
Keyword(s):  
Reinforcement Learning ◽  
Information Fusion ◽  
Information Quality ◽  
Uncertain Information ◽  
Dempster Shafer Theory ◽  
Conflicting Evidence ◽  
Basic Probability ◽  
Sensor Information ◽  
Shafer Theory ◽  
Deng Entropy

Dempster–Shafer theory (DST), which is widely used in information fusion, can process uncertain information without prior information; however, when the evidence to combine is highly conflicting, it may lead to counter-intuitive results. Moreover, the existing methods are not strong enough to process real-time and online conflicting evidence. In order to solve the above problems, a novel information fusion method is proposed in this paper. The proposed method combines the uncertainty of evidence and reinforcement learning (RL). Specifically, we consider two uncertainty degrees: the uncertainty of the original basic probability assignment (BPA) and the uncertainty of its negation. Then, Deng entropy is used to measure the uncertainty of BPAs. Two uncertainty degrees are considered as the condition of measuring information quality. Then, the adaptive conflict processing is performed by RL and the combination two uncertainty degrees. The next step is to compute Dempster’s combination rule (DCR) to achieve multi-sensor information fusion. Finally, a decision scheme based on correlation coefficient is used to make the decision. The proposed method not only realizes adaptive conflict evidence management, but also improves the accuracy of multi-sensor information fusion and reduces information loss. Numerical examples verify the effectiveness of the proposed method.

scholarly journals Contributions of the basal ganglia to action sequence learning and performance

2019 ◽  
Author(s):  
Eric Garr
Keyword(s):  
Reinforcement Learning ◽  
Basal Ganglia ◽  
Sequence Learning ◽  
Computational Framework ◽  
Action Sequence ◽  
Action Sequences ◽  
The Hierarchical Structure ◽  
And Performance ◽  
Action Sequencing

Animals engage in intricately woven and choreographed action sequences that are constructed from trial-and-error learning. The mechanisms by which the brain links together individual actions which are later recalled as fluid chains of behavior are not fully understood, but there is broad consensus that the basal ganglia play a crucial role in this process. This paper presents a comprehensive review of the role of the basal ganglia in action sequencing, with a focus on whether the computational framework of reinforcement learning can capture key behavioral features of sequencing and the neural mechanisms that underlie them. While a simple neurocomputational model of reinforcement learning can capture key features of action sequence learning, this model is not sufficient to capture goal-directed control of sequences or their hierarchical representation. The hierarchical structure of action sequences, in particular, poses a challenge for building better models of action sequencing, and it is in this regard that further investigations into basal ganglia information processing may be informative.

Sign in / Sign up

Export Citation Format

Share Document