Michalis T. Tsapanos

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Real Time Strategy games (RTS) provide an interesting test bed for agents that use Reinforcement Learning (RL) algorithms. From an agent's point of view, RTS games constitute a Markovian, partially observable and dynamic environment with a huge state space. In this paper, we present an agent that uses a Zeroth-level Classifier System (ZCS) in order to(More)
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