Brain Inspired Meta Reinforcement Learning Using Brain-Inspired Networks, M.Sc. Thesis Sharif University of Technology ; Soleymani Baghshahi, Mahdih (Supervisor)
Abstract
Reinforcement learning is one of the most well-known learning paradigms in biological agents and one of the most used ones for solving plenty of problems. One of the reasons for this widespread use is the low demand for supervising signals. However, the sparsity of the reward signal causes increasing in sample complexity that needs for learning new tasks. This issue makes trouble in multi-task settings, specifically.One of the most promising approaches to learning new tasks by limited interaction with the environment is meta reinforcement learning. An approach in which fast adaption becomes possible by limiting hypothesis space and creating inductive biases by learning meta parameters....
Cataloging briefBrain Inspired Meta Reinforcement Learning Using Brain-Inspired Networks, M.Sc. Thesis Sharif University of Technology ; Soleymani Baghshahi, Mahdih (Supervisor)
Abstract
Reinforcement learning is one of the most well-known learning paradigms in biological agents and one of the most used ones for solving plenty of problems. One of the reasons for this widespread use is the low demand for supervising signals. However, the sparsity of the reward signal causes increasing in sample complexity that needs for learning new tasks. This issue makes trouble in multi-task settings, specifically.One of the most promising approaches to learning new tasks by limited interaction with the environment is meta reinforcement learning. An approach in which fast adaption becomes possible by limiting hypothesis space and creating inductive biases by learning meta parameters....
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