Notes from Ammar/Mocanu – Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines

Notes from Ammar/Mocanu – Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines

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Citation H. Ammar and D. Mocanu, “Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines,” Mach. Learn. …, 2013. Abstract Existing reinforcement learning approaches are often hampered by learning tabula rasa.  Transfer for reinforcement learning tackles this problem by enabling the reuse of previously learned results, but may require an inter-task mapping to encode how the previously learned task and the new task are related.  This paper presents an autonomous framework for learning inter-task mappings based on an adaptation of restricted Boltzmann machines.  Both a full model and a computationally efficient factored model are introduced and shown to be effective in multiple transfer learning scenarios. Quotes & Notes Re:Random or not Unfortunately, learning in this model cannot be done with normal CD. The main reason is that if…
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