A Framework for Integrating Heterogeneous Learning Agents

1993 ◽  
pp. 746-764 ◽  
Author(s):  
Bernard Silver ◽  
John Vittal ◽  
William Frawley ◽  
Glenn Iba ◽  
Tom Fawcett ◽  
...  
Keyword(s):  
Learning Agents ◽  
Heterogeneous Learning

INTELLIGENT AND COOPERATIVE INFORMATION SYSTEMS MEET MACHINE LEARNING

1992 ◽  
Vol 01 (02) ◽  
pp. 347-361 ◽  
Author(s):  
JOHN VITTAL ◽  
BERNARD SILVER ◽  
WILLIAM FRAWLEY ◽  
GLENN IBA ◽  
TOM FAWCETT ◽  
...  
Keyword(s):  
Information Systems ◽  
Learning System ◽  
Integrated Learning System ◽  
Learning Agents ◽  
Central Controller ◽  
Large Numbers ◽  
Heterogeneous Learning ◽  
Cooperative Information Systems ◽  
Problem Solvers ◽  
High Level

Intelligent and Cooperative Information Systems (ICIS) will have large numbers of distributed, heterogeneous agents interacting and cooperating to solve problems regardless of location, original mission, or platform. The agents in an ICIS will adapt to new and possibly surprising situations, preferably without human intervention. These systems will not only control a domain, but also will improve their own performance over time, that is, they will learn. This paper describes five heterogeneous learning agents and how they are integrated into an Integrated Learning System (ILS) where some of the agents cooperate to improve performance. The issues involve coordinating distributed, cooperating, heterogeneous problem-solvers, combining various learning paradigms, and integrating different reasoning techniques. ILS also includes a central controller, called The Learning Coordinator (TLC), that manages the control of flow and communication among the agents, using a high-level communication protocol. In order to demonstrate the generality of the ILS architecture, we implemented an application which, through its own experience, learns how to control the traffic in a telephone network, and show the results for one set of experiments. Options for enhancements of the ILS architecture are also discussed.

scholarly journals An Evaluation Methodology for Interactive Reinforcement Learning with Simulated Users

Biomimetics ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Adam Bignold ◽  
Francisco Cruz ◽  
Richard Dazeley ◽  
Peter Vamplew ◽  
Cameron Foale
Keyword(s):  
Reinforcement Learning ◽  
Information Source ◽  
Human Interaction ◽  
Evaluation Methodology ◽  
External Information ◽  
Preliminary Evaluation ◽  
Learning Agents ◽  
Learning Agent ◽  
Knowledge Bias ◽  
The Impact

Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient sample size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent.

scholarly journals An Efficiency Enhancing Methodology for Multiple Autonomous Vehicles in an Urban Network Adopting Deep Reinforcement Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1514 ◽  
Author(s):  
Quang-Duy Tran ◽  
Sang-Hoon Bae
Keyword(s):  
Reinforcement Learning ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Effective Means ◽  
Urban Network ◽  
Learning Agents ◽  
Policy Optimization ◽  
The Impact

To reduce the impact of congestion, it is necessary to improve our overall understanding of the influence of the autonomous vehicle. Recently, deep reinforcement learning has become an effective means of solving complex control tasks. Accordingly, we show an advanced deep reinforcement learning that investigates how the leading autonomous vehicles affect the urban network under a mixed-traffic environment. We also suggest a set of hyperparameters for achieving better performance. Firstly, we feed a set of hyperparameters into our deep reinforcement learning agents. Secondly, we investigate the leading autonomous vehicle experiment in the urban network with different autonomous vehicle penetration rates. Thirdly, the advantage of leading autonomous vehicles is evaluated using entire manual vehicle and leading manual vehicle experiments. Finally, the proximal policy optimization with a clipped objective is compared to the proximal policy optimization with an adaptive Kullback–Leibler penalty to verify the superiority of the proposed hyperparameter. We demonstrate that full automation traffic increased the average speed 1.27 times greater compared with the entire manual vehicle experiment. Our proposed method becomes significantly more effective at a higher autonomous vehicle penetration rate. Furthermore, the leading autonomous vehicles could help to mitigate traffic congestion.

Modeling human-like longitudinal driver model for intelligent vehicles based on reinforcement learning

2021 ◽  
pp. 095440702098357
Author(s):  
Ju Xie ◽  
Xing Xu ◽  
Feng Wang ◽  
Haobin Jiang
Keyword(s):  
Reinforcement Learning ◽  
Comprehensive Evaluation ◽  
Path Following ◽  
Intelligent Vehicles ◽  
Driver Model ◽  
Control Center ◽  
Training Performance ◽  
Learning Agents ◽  
System A ◽  
And Control

The driver model is the decision-making and control center of intelligent vehicle. In order to improve the adaptability of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the skilled driver under the driver-vehicle-road closed-loop system, a kind of human-like longitudinal driver model for intelligent vehicles based on reinforcement learning is proposed. This paper builds the lateral driver model for intelligent vehicles based on optimal preview control theory. Then, the control correction link of longitudinal driver model is established to calculate the throttle opening or brake pedal travel for the desired longitudinal acceleration. Moreover, the reinforcement learning agents for longitudinal driver model is parallel trained by comprehensive evaluation index and skilled driver data. Lastly, training performance and scenarios verification between the simulation experiment and the real car test are performed to verify the effectiveness of the reinforcement learning based longitudinal driver model. The results show that the proposed human-like longitudinal driver model based on reinforcement learning can help intelligent vehicles effectively imitate the speed control behavior of the skilled driver in various path-following scenarios.

Sign in / Sign up

Export Citation Format

Share Document