Simulation with learning agents

    Authors

    E. Gelenbe; E. Seref;Z. G. Xu

    Comments

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    Abbreviated Journal Title

    Proc. IEEE

    Keywords

    adaptive entities; goal-based adaptation; learning agents; random neural; networks; reinforcement learning; simulation; RANDOM NEURAL-NETWORK; Engineering, Electrical & Electronic

    Abstract

    We propose that learning agents (LAs) be incorporated into simulation environments in order to model the adaptive behavior of humans. These LAs adapt to specific circumstances and events during the simulation run. They would select tasks to be accomplished among a given set of tasks 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 ail approach in which agents are assigned goals when the simulation starts and then pursue these goals autonomously and adaptively. 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 networks can support this approach and shows that goal-based learning,may be used effectively used in this contest. 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.

    Journal Title

    Proceedings of the Ieee

    Volume

    89

    Issue/Number

    2

    Publication Date

    1-1-2001

    Document Type

    Article

    Language

    English

    First Page

    148

    Last Page

    157

    WOS Identifier

    WOS:000167518000003

    ISSN

    0018-9219

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