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Problem Solving in a Dynamic Environment
This dissertation studies how different learning architectures perform in a dynamic environment: an arena containing a bouncing ball, which the agent must catch. In particular, it adds functionality to the agent in a way as to imitate a perceived hierarchy of cognitive faculties possessed by sentient minds. A key role is played by the ability to generalise from learning experiences, and this study attempts to combine reinforcement learning with neural networks to this end. This environment<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1142/9789812831507_0008">doi:10.1142/9789812831507_0008</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jklqlbu6knh6ndp2wh2u4yjmxi">fatcat:jklqlbu6knh6ndp2wh2u4yjmxi</a> </span>
more »... strates the benefits to a table-based learner of short-term memory, a generalising state representation and planning with a Dyna architecture. In contrast, the development of a working neural net-based architecture encountered significant problems. i
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