Towards the application of reinforcement learning to undirected developmental learning

  • Mugan J
  • Kuipers B
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We consider the problem of how a learn- ing agent in a continuous and dynamic world can autonomously learn about itself, its en- vironment, and how to perform simple ac- tions. In previous work we showed how an agent could learn an abstraction consisting of contingencies and distinctions. In this paper we propose a method whereby an agent using this abstraction can create its own reinforce- ment learning problems. The agent generates an internal signal that motivates it to move into states in which a contingency will hold. The agent then uses reinforcement learning to learn to move to those states effectively. It can

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  • J. Mugan

  • B. Kuipers

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