Publications

Low-Rank Tensor-Network Encodings for Video-to-Action Behavioral Cloning

Under review (TMLR), 2024

We describe a tensor-network latent-space encoding approach for increasing the scalability of behavioral cloning of a video game player’s actions entirely from video streams of the gameplay. Specifically, we address challenges associated with the high computational requirements of traditional deep-learning based encoders such as variational autoencoders that prohibit their use in widely available hardware or for large scale data. Our approach uses tensor networks instead of deep variational autoencoders for this purpose, and it yields significant speedups with no loss of accuracy. Empirical results on ATARI games demonstrate that our approach leads to a speedup in the time it takes to encode data and train a predictor using the encodings (between 2.6× to 9.6× compared to autoencoders or variational autoencoders). Furthermore, the tensor train encoding can be efficiently trained on CPU as well, which leads to comparable or better training times than the autoencoder and variational autoencoder trained on GPU (0.9× to 5.4× faster). These results suggest significant possibilities in mitigating the need for cost and time-intensive hardware for training deep-learning architectures for behavioral cloning. [Paper]

Numerical and geometrical aspects of flow-based variational quantum Monte Carlo

Machine Learning: Science and Technology, 2022

This article aims to summarize recent and ongoing efforts to simulate continuous-variable quantum systems using flow-based variational quantum Monte Carlo techniques, focusing for pedagogical purposes on the example of bosons in the field amplitude (quadrature) basis. Particular emphasis is placed on the variational real- and imaginary-time evolution problems, carefully reviewing the stochastic estimation of the time-dependent variational principles and their relationship with information geometry. Some practical instructions are provided to guide the implementation of a PyTorch code. The review is intended to be accessible to researchers interested in machine learning and quantum information science. [Paper]

Overcoming barriers to scalability in variational quantum Monte Carlo

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2021

The variational quantum Monte Carlo (VQMC) method received significant attention in the recent past because of its ability to overcome the curse of dimensionality inherent in many-body quantum systems. Close parallels exist between VQMC and the emerging hybrid quantum-classical computational paradigm of variational quantum algorithms. VQMC overcomes the curse of dimensionality by performing alternating steps of Monte Carlo sampling from a parametrized quantum state followed by gradient-based optimization. While VQMC has been applied to solve high-dimensional problems, it is known to be difficult to parallelize, primarily owing to the Markov Chain Monte Carlo (MCMC) sampling step. In this work, we explore the scalability of VQMC when autoregressive models, with exact sampling, are used in place of MCMC. This approach can exploit distributed-memory, shared-memory and/or GPU parallelism in the sampling task without any bottlenecks. In particular, we demonstrate GPU-scalability of VQMC for solving up to ten-thousand dimensional combinatorial optimization problems. [Paper]

Behavioral cloning in Atari games using a combined variational autoencoder and predictor model

IEEE CEC 2021 Session on Games, 2021

We explore an approach to behavioral cloning in video games. We are motivated to pursue a learning architecture that is data efficient and provides opportunity for interpreting player strategies and replicating player actions in unseen situations. To this end, we have developed a generative model that learns latent features of a game that can be used for training an action predictor. Specifically, our architecture combines a Variational Autoencoder with a discriminator mapping the latent space to action predictions (predictor). We compare our model performance to two different behavior cloning architectures: a discriminative model (a Convolutional Neural Network) mapping game states directly to actions, and a Variational Autoencoder with a predictor trained separately. Finally, we demonstrate how we can use the advantage of generative modeling to sample new states from the latent space of the Variational Autoencoder to analyze player actions and provide meaning to certain latent features. [Paper] [Code]

Meta-variational Monte Carlo

Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), 2020

An identification is found between meta-learning and the problem of determining the ground state of a randomly generated Hamiltonian drawn from a known ensemble. A model-agnostic meta-learning approach is proposed to solve the associated learning problem and a preliminary experimental study of random Max-Cut problems indicates that the resulting Meta Variational Monte Carlo accelerates training and improves convergence. [Paper]

Brian Chen