The released agent from Playing to Par: Reinforcement Learning for Provably Optimal Quadrilateral Block Decompositions (Arjun Narayanan and Per-Olof Persson, UC Berkeley). The agent builds a quadrilateral block decomposition from a 2D domain's bare boundary. It aims for par: the lower bound on vertex irregularity that the discrete Gauss–Bonnet identity fixes from the domain's corner angles and topology alone, I(M) ≥ par(Ω) = |c(Ω) + 4χ|. A mesh that reaches par is provably optimal in its connectivity. into demonstrations), followed by 4,004,096 PPO steps. edge list) that follows each half-edge's next, previous and twin. It has 4 layers of 96 features on 10 input features per half-edge.…
Independent publisher
Arjun Narayanan
arjunnarayanan
Medical AI, reinforcement learning, graph neural networks
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