| Mapping the Design Space of Reinforcement Learning Problems – a Case Study (2008) | |||||||||||||||
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| This paper reports on a case study motivated by a typical reinforcement learning problem in robotics: an overall goal which decomposes into several subgoals has to be reached in a discrete large sized state space. For simplicity, we model this problem in a standard gridworld setting and perform an extensive comparison of different parameter and design choices. During this, we focus on the central role of the representation of the state space. We examine three fundamentally different representations with counterparts in “real life” robotics. We investigate their behaviour with respect to (i) the size and properties of the state space, (ii) different exploration strategies including the recent proposal of multistep-actions and (iii) the type and parameters of the reward function. 1 | |||||||||||||||
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