Solving planning problems with deep reinforcement learning and tree search
Ge, Victor
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https://hdl.handle.net/2142/101086
Description
Title
Solving planning problems with deep reinforcement learning and tree search
Author(s)
Ge, Victor
Issue Date
2018-04-26
Director of Research (if dissertation) or Advisor (if thesis)
Lazebnik, Svetlana
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
reinforcement learning
mcts
sokoban
a*
heuristic
Abstract
Deep reinforcement learning methods are capable of learning complex heuristics starting with no prior knowledge, but struggle in environments where the learning signal is sparse. In contrast, planning methods can discover the optimal path to a goal in the absence of external rewards, but often require a hand-crafted heuristic function to be effective. In this thesis, we describe a model-based reinforcement learning method that bridges the middle ground between these two approaches. When evaluated on the complex domain of Sokoban, the model-based method was found to be more performant, stable and sample-efficient than a model-free baseline.
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