Coevolutionary Learning of Neuromodulated Controllers for Multi-Stage and Gamified Tasks
Coevolutionary Learning of Neuromodulated Controllers for Multi-Stage and Gamified Tasks
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DOI：
10.1109/acsos49614.2020.00034
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Chloe M. Barnes;Anikó Ekárt;K. Ellefsen;K. Glette;Peter R. Lewis;J. Tørresen
中科院分区：
文献类型：
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作者：
Chloe M. Barnes;Anikó Ekárt;K. Ellefsen;K. Glette;Peter R. Lewis;J. Tørresen
神经网络已被广泛用于智能体学习架构，然而，学习多个上下文相关的任务同时或顺序使用它们时是有问题的。行为可塑性使人类和动物都能对环境和环境刺激的变化做出反应，而不会降低学习的知识;这可以通过神经调节来调节行为来实现-神经调节是大脑中发现的生物过程。我们证明了在进化神经网络时调制活动传播信号使代理能够更容易地学习上下文相关和多阶段任务。此外，我们表明，这种好处是保存时，代理占据与其他神经调节剂共享的环境。此外，我们表明，神经调节帮助代理人已经单独进化，以适应环境刺激的变化时，他们继续在一个共享的环境中发展。
Neural networks have been widely used in agent learning architectures; however, learning multiple context dependent tasks simultaneously or sequentially is problematic when using them. Behavioural plasticity enables humans and animals alike to respond to changes in context and environmental stimuli, without degrading learnt knowledge; this can be achieved by regulating behaviour with neuromodulation – a biological process found in the brain. We demonstrate that modulating activity-propagating signals when evolving neural networks enables agents to learn context-dependent and multi-stage tasks more easily. Further, we show that this benefit is preserved when agents occupy an environment shared with other neuromodulated agents. Additionally we show that neuromodulation helps agents that have evolved alone to adapt to changes in environmental stimuli when they continue to evolve in a shared environment.