Q-Cog: a Q-Learning based cognitive agent architecture for complex 3D virtual worlds.
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Date
2017
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Abstract
Intelligent cognitive agents should be able to autonomously gather new knowledge and
learn from their own experiences in order to adapt to a changing environment. 3D virtual
worlds provide complex environments in which autonomous software agents may
learn and interact. In many applications within this domain, such as video games and
virtual reality, the environment is partially observable and agents must make decisions
and react in real-time. Due to the dynamic nature of virtual worlds, adaptability is of
great importance for virtual agents. The Reinforcement Learning paradigm provides a
mechanism for unsupervised learning that allows agents to learn from their own experiences
in the environment. In particular, the Q-Learning algorithm allows agents to
develop an optimal action-selection policy based on their experiences. This research
explores the adaptability of cognitive architectures using Reinforcement Learning to
construct and maintain a library of action-selection policies. The proposed cognitive
architecture, Q-Cog, utilizes a policy selection mechanism to develop adaptable
3D virtual agents. Results from experimentation indicates that Q-Cog provides an
effective basis for developing adaptive self-learning agents for 3D virtual worlds.
Description
Master of Science in Computer Science, University of KwaZulu-Natal, Westville, 2017.