Ross Michael Anderson
Ross Anderson is a software engineer at Google Research on the operations research team in Cambridge MA. His recent work has been on problems in the intersection of optimization and machine learning. His personal website can be found here. He obtained his PhD from the MIT Operations Research Center in 2014.
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CaQL: Continuous Action Q-Learning
Moonkyung Ryu
Proceedings of the Eighth International Conference on Learning Representations (ICLR-20), Addis Ababa, Ethiopia (2020)
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In this work we propose CaQL, a value-based reinforcement learning (RL) algorithm that handles continuous actions, whose Q-function is modeled by a generic feed-forward neural network. We show that the problem of calculating Bellman residual can be posed as a mixed-integer linear programming (MILP) problem. Furthermore to reduce the complexity of computing Bellman residual, we propose three techniques (i) dynamic tolerance, (ii) dual filter, (iii) clustering to speed up the computation of max-Q values. Finally, to illustrate the efficiency of CaQL, we compare it with state-of-the-art RL algorithms on benchmark continuous control problems that have various action constraints, and show that CaQL significantly outperforms policy-based methods in heavily constrained environments.
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The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification
Joey Huchette
Krunal K Patel
Will Ma
Advances in Neural Information Processing Systems (2020), pp. 21675-21686
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We improve the effectiveness of propagation- and linear-optimization-based neural network verification algorithms with a new tightened convex relaxation for ReLU neurons. Unlike previous single-neuron relaxations which focus only on the univariate input space of the ReLU, our method considers the multivariate input space of the affine pre-activation function preceding the ReLU. Using results from submodularity and convex geometry, we derive an explicit description of the tightest possible convex relaxation when this multivariate input is over a box domain. We show that our convex relaxation is significantly stronger than the commonly used univariate-input relaxation which has been proposed as a natural convex relaxation barrier for verification. While our description of the relaxation may require an exponential number of inequalities, we show that they can be separated in linear time and hence can be efficiently incorporated into optimization algorithms on an as-needed basis. Based on this novel relaxation, we design two polynomial-time algorithms for neural network verification: a linear-programming-based algorithm that leverages the full power of our relaxation, and a fast propagation algorithm that generalizes existing approaches. In both cases, we show that for a modest increase in computational effort, our strengthened relaxation enables us to verify a significantly larger number of instances compared to similar algorithms.
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Strong mixed-integer programming formulations for trained neural networks
Will Ma
Mathematical Programming, 183 (2020), pp. 3-39
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We present strong mixed-integer programming (MIP) formulations for high-dimensional piecewise linear functions that correspond to trained neural networks. These formulations can be used for a number of important tasks, such as verifying that an image classification network is robust to adversarial inputs, or solving decision problems where the objective function is a machine learning model. We present a generic framework, which may be of independent interest, that provides a way to construct sharp or ideal formulations for the maximum of d affine functions over arbitrary polyhedral input domains. We apply this result to derive MIP formulations for a number of the most popular nonlinear operations (e.g. ReLU and max pooling) that are strictly stronger than other approaches from the literature. We corroborate this computationally, showing that our formulations are able to offer substantial improvements in solve time on verification tasks for image classification networks.
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Reinforcement Learning with Combinatorial Actions: An Application to the Capacitated Vehicle Routing Problem
Arthur Delarue
Advances in Neural Information Processing Systems (2020)
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Value-function-based methods have long played an important role in reinforcement learning. However, finding the best next action given a value function of arbitrary complexity is nontrivial when the action space is too large for enumeration. We develop a framework for value-function-based deep reinforcement learning with a combinatorial action space, in which the action selection problem is explicitly formulated as a mixed-integer optimization problem. As a motivating example, we present an application of this framework to the capacitated vehicle routing problem (CVRP), a combinatorial optimization problem in which a set of locations must be covered by a single vehicle with limited capacity. On each instance, we model an action as the construction of a single route, and consider a deterministic policy which is improved through a simple policy iteration algorithm. Our approach is competitive with other reinforcement learning methods and achieves an average gap of 1.7% with state-of-the-art OR methods on standard library instances of medium size.
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Strong mixed-integer programming formulations for trained neural networks
Integer Programming and Combinatorial Optimization - 20th International Conference, IPCO 2019, Ann Arbor, MI, USA, May 22-24, 2019, Proceedings, Springer, pp. 27-42
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We present an ideal mixed-integer programming (MIP) formulation for a rectified linear unit (ReLU) appearing in a trained neural network. Our formulation requires a single binary variable and no additional continuous variables beyond the input and output variables of the ReLU. We contrast it with an ideal “extended” formulation with a linear number of additional continuous variables, derived through standard techniques. An apparent drawback of our formulation is that it requires an exponential number of inequality constraints, but we provide a routine to separate the inequalities in linear time. We also prove that these exponentially-many constraints are facet-defining under mild conditions. Finally, we study network verification problems and observe that dynamically separating from the exponential inequalities (1) is much more computationally efficient and scalable than the extended formulation, (2) decreases the solve time of a state-of-the-art MIP solver by a factor of 7 on smaller instances, and (3) nearly matches the dual bounds of a state-of-the-art MIP solver on harder instances, after just a few rounds of separation and in orders of magnitude less time.
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