Learning and behaviour of mobile robots faces limitations. In reinforcement learning, for example, an agent learns a strategy to get to only one specific target point within a state space. However, we can grasp a visually localized object at any point in space or navigate to any position in a room. We present a neural network model in which an agent learns a model of the state space that allows him to get to an arbitrarily chosen goal via a short route. By randomly exploring the state space, the agent learns associations between two adjoining states and the action that links them. Given arbitrary starting and goal positions, route-finding is done in two steps. First, an activation gradient spreads around the goal position along the associative connections. Second, the agent uses state-action associations to determine the actions leading to ascend the gradient toward the goal. All mechanisms are biologically justifiable. © Springer-Verlag Berlin Heidelberg 2008.
CITATION STYLE
Weber, C., & Triesch, J. (2008). From exploration to planning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5163 LNCS, pp. 740–749). https://doi.org/10.1007/978-3-540-87536-9_76
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