Deepmind. 11/2019. “
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model”.
Publisher's VersionAbstractA long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from selfplay. Here, we introduce an algorithm based solely on reinforcement learning, without human data, guidance, or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo.
Jia Chen, Jiayi Wei, Yu Feng, Osbert Bastani, and Isil Dillig. 10/2019. “
Relational Verification using Reinforcement Learning.” PACML, OOPSLA.
PDFAbstractRelational verification aims to prove properties that relate a pair of programs or two different runs of the same program. While relational properties (e.g., equivalence, non-interference) can be verified by reducing them to standard safety, there are typically many possible reduction strategies, only some of which result in successful automated verification. Motivated by this problem, we propose a novel relational verification algorithm that learns useful reduction strategies using reinforcement learning. Specifically, we show how to formulate relational verification as a Markov Decision Process (MDP) and use reinforcement learning to synthesize an optimal policy for the underlying MDP. The learned policy is then used to guide the search for a successful verification strategy. We have implemented this approach in a tool called Coeus and evaluate it on two benchmark suites. Our evaluation shows that Coeus solves significantly more problems within a given time limit compared to multiple baselines, including two state-of-the-art relational verification tools.
Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, and Jiajun Wu. 1/2019. “
Learning to Infer and Execute 3D Shape Programs.” ICLR.
PDFAbstractHuman perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape structure such as the repetition and reflective symmetry of object parts. In contrast, recent advances in 3D shape sensing focus more on low-level geometry but less on these higher-level relationships. In this paper, we propose 3D shape programs, integrating bottom-up recognition systems with top-down, symbolic program structure to capture both low-level geometry and high-level structural priors for 3D shapes. Because there are no annotations of shape programs for real shapes, we develop neural modules that not only learn to infer 3D shape programs from raw, unannotated shapes, but also to execute these programs for shape reconstruction. After initial bootstrapping, our end-to-end differentiable model learns 3D shape programs by reconstructing shapes in a self-supervised manner. Experiments demonstrate that our model accurately infers and executes 3D shape programs for highly complex shapes from various categories. It can also be integrated with an image-to-shape module to infer 3D shape programs directly from an RGB image, leading to 3D shape reconstructions that are both more accurate and more physically plausible.