Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Structure in the Space of Value Functions
Machine Learning
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Learning forward models for robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
IEEE Computational Intelligence Magazine
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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.