Journal ArticleDOI
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TLDR
Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.Abstract:
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of stateof-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.read more
Citations
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Journal ArticleDOI
Hierarchical reinforcement learning for self-driving decision-making without reliance on labelled driving data
TL;DR: This study presents a hierarchical reinforcement learning method for decision making of self-driving cars, which does not depend on a large amount of labelled driving data and comprehensively considers both high-level manoeuvre selection and low-level motion control in both lateral and longitudinal directions.
Proceedings Article
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
TL;DR: A novel model-based reinforcement learning framework for recommendation systems is proposed, where a generative adversarial network is developed to imitate user behavior dynamics and learn her reward function, and the RL policy based on this model can lead to a better long-term reward for the user and higher click rate for the system.
Journal ArticleDOI
Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications
TL;DR: This work constructs a Deep Recurrent Q-Network model which is a Recurrent Neural Network deep learning algorithm over the Q-Learning reinforcement learning algorithm to forecast the crop yield, outperforming existing models by preserving the original data distribution.
Posted Content
Generative Language Modeling for Automated Theorem Proving.
Stanislas Polu,Ilya Sutskever +1 more
TL;DR: This work presents an automated prover and proof assistant, GPT-f, for the Metamath formalization language, and analyzes its performance, finding new short proofs that were accepted into the mainMetamath library, which is to this knowledge, the first time a deep-learning based system has contributed proofs that are adopted by a formal mathematics community.
Posted Content
Latent Intention Dialogue Models
TL;DR: This paper proposed a Latent Intention Dialogue Model (LIDM) that employs a discrete latent variable to learn underlying dialogue intentions in the framework of neural variational inference, which can be interpreted as actions guiding the generation of machine responses.
References
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Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
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