Title

Simulation With Learning Agents

Keywords

Adaptive entities; Goal-based adaptation; Learning agents; Random neural networks; Reinforcement learning; Simulation

Abstract

We propose that learning agents (LAs) be incorporated into simulation environments in order to model the adaptive behavior of hunans. These LAs adapt to specific circumstances and events daring the simulation run. They would select tasks to be accomplished among a given set of tusks as the simulation progresses, or synthesize tasks for themselves based on their observations of the environment and on information they may receive from other agents. We investigate an approach in which agents are assigned goals when the simulation starts and then pursue these goals autonomously and adoptively. During the simulation, agents progressively improve their ability to accomplish their goals effectively and safely. Agents learn from their own observations and from the experience of other agents with whom they exchange information. Each LA starts with a given representation of the simulation environment from which it progressively constructs its own internal representation and uses it to make decisions. This paper describes how learning neural nemorks can support this approach and shows that goal-based learning may be used effectively used in this context. An example simulation is presented in 'which agents represent manned vehicles; they are assigned the goal of traversing a dangerous metropolitan grid safely and rapidly using goal-based reinforcement learning with neural networks and compared to three other algorithms. © 2001 IEEE.

Publication Date

1-1-2001

Publication Title

Proceedings of the IEEE

Volume

89

Issue

2

Number of Pages

148-157

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/5.910851

Socpus ID

0000997490 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0000997490

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