Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2110.15481v1
• Date: Fri, 29 Oct 2021 01:09:51 GMT
• Title: Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
• Authors: Hyunsoo Chung, Jungtaek Kim, Boris Knyazev, Jinhwi Lee, Graham W. Taylor, Jaesik Park, Minsu Cho
• Abstract summary: We introduce a novel formulation, complex construction, which requires a building agent to assemble unit primitives sequentially. To construct a target object, we provide incomplete knowledge about the desired target (i.e., 2D images) instead of exact and explicit information to the agent. We demonstrate that the proposed method successfully learns to construct an unseen object conditioned on a single image or multiple views of a target object.
• Score: 52.85981207514049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Discovering a solution in a combinatorial space is prevalent in many real-world problems but it is also challenging due to diverse complex constraints and the vast number of possible combinations. To address such a problem, we introduce a novel formulation, combinatorial construction, which requires a building agent to assemble unit primitives (i.e., LEGO bricks) sequentially -- every connection between two bricks must follow a fixed rule, while no bricks mutually overlap. To construct a target object, we provide incomplete knowledge about the desired target (i.e., 2D images) instead of exact and explicit volumetric information to the agent. This problem requires a comprehensive understanding of partial information and long-term planning to append a brick sequentially, which leads us to employ reinforcement learning. The approach has to consider a variable-sized action space where a large number of invalid actions, which would cause overlap between bricks, exist. To resolve these issues, our model, dubbed Brick-by-Brick, adopts an action validity prediction network that efficiently filters invalid actions for an actor-critic network. We demonstrate that the proposed method successfully learns to construct an unseen object conditioned on a single image or multiple views of a target object.

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