ITS Berkeley

Block Simplex Signal Recovery: Methods, Trade-Offs, and an Application to Routing

Wu, Cathy
Pozdnukhov, Alexey
Bayen, Alexandre M.
2020

This paper presents the problem of block simplex constrained signal recovery, which has been demonstrated to be a suitable formulation for estimation problems in networks such as route flow estimation in traffic. There are several natural approaches to this problem: compressed sensing, Bayesian inference, and convex optimization. This paper presents new methods within each framework and assesses their respective abilities to reconstruct signals, with the particular emphasis on sparse recovery, ability to incorporate prior information, and scalability. We then apply these methods to route...

Routing on Traffic Networks Incorporating Past Memory up to Real-Time Information on the Network State

Keimer, Alexander
Bayen, Alexandre
2020

In this review, we discuss routing algorithms for the dynamic traffic assignment (DTA) problem that assigns traffic flow in a given road network as realistically as possible. We present a new class of so-called routing operators that route traffic flow at intersections based on either real-time information about the status of the network or historical data. These routing operators thus cover the distribution of traffic flow at all possible intersections. To model traffic flow on the links, we use a well-known macroscopic ordinary delay differential equation. We prove the existence and...

BISTRO: Berkeley Integrated System for Transportation Optimization

Feygin, Sidney A.
Lazarus, Jessica
Forscher, Edward H.
Golfier-Vetterli, Valentine
Lee, Jonathan W.
Gupta, Abhishek
Bayen, Alexandre
2020

The current trend toward urbanization and adoption of flexible and innovative mobility technologies will have complex and difficult-to-predict effects on urban transportation systems. Comprehensive methodological frameworks that account for the increasingly uncertain future state of the urban mobility landscape do not yet exist. Furthermore, few approaches have enabled the massive ingestion of urban data in planning tools capable of offering the flexibility of scenario-based design.This article introduces Berkeley Integrated System for Transportation Optimization (BISTRO), a new open...

Robust Reinforcement Learning using Adversarial Populations

Vinitsky, Eugene
Du, Yuqing
Parvate, Kanaad
Jang, Kathy
Abbeel, Pieter
Bayen, Alexandre
2020

Reinforcement Learning (RL) is an effective tool for controller design but can struggle with issues of robustness, failing catastrophically when the underlying system dynamics are perturbed. The Robust RL formulation tackles this by adding worst-case adversarial noise to the dynamics and constructing the noise distribution as the solution to a zero-sum minimax game. However, existing work on learning solutions to the Robust RL formulation has primarily focused on training a single RL agent against a single adversary. In this work, we demonstrate that using a single adversary does not...

Zero-Shot Autonomous Vehicle Policy Transfer: From Simulation to Real-World via Adversarial Learning

Chalaki, Behdad
Beaver, Logan E.
Remer, Ben
Jang, Kathy
Vinitsky, Eugene
Bayen, Alexandre
Malikopoulos, Andreas A.
2020

In this article, we demonstrate a zero-shot transfer of an autonomous driving policy from simulation to University of Delaware's scaled smart city with adversarial multi-agent reinforcement learning, in which an adversary attempts to decrease the net reward by perturbing both the inputs and outputs of the autonomous vehicles during training. We train the autonomous vehicles to coordinate with each other while crossing a roundabout in the presence of an adversary in simulation. The adversarial policy successfully reproduces the simulated behavior and incidentally outperforms, in terms of...

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms

Yu, Chao
Velu, Akash
Vinitsky, Eugene
Wang, Yu
Bayen, Alexandre
Wu, Yi
2020

We benchmark commonly used multi-agent deep reinforcement learning (MARL) algorithms on a variety of cooperative multi-agent games. While there has been significant innovation in MARL algorithms, algorithms tend to be tested and tuned on a single domain and their average performance across multiple domains is less characterized. Furthermore, since the hyperparameters of the algorithms are carefully tuned to the task of interest, it is unclear whether hyperparameters can easily be found that allow the algorithm to be repurposed for other cooperative tasks with different reward structure and...

Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RL

Vinitsky, Eugene
Lichtle, Nathan
Parvate, Kanaad
Bayen, Alexandre
2020

We study the ability of autonomous vehicles to improve the throughput of a bottleneck using a fully decentralized control scheme in a mixed autonomy setting. We consider the problem of improving the throughput of a scaled model of the San Francisco-Oakland Bay Bridge: a two-stage bottleneck where four lanes reduce to two and then reduce to one. Although there is extensive work examining variants of bottleneck control in a centralized setting, there is less study of the challenging multi-agent setting where the large number of interacting AVs leads to significant optimization difficulties...

Hamilton-Jacobi Formulation for State-Constrained Optimal Control and Zero-Sum Game Problems

Lee, Donggun
Keimer, Alexander
Bayen, Alexandre M.
Tomlin, Claire J.
2020

This paper presents a general Hamilton-Jacobi (HJ) framework for optimal control and two-player zero-sum game problems, both with state constraints. In the optimal control problem, a control signal and terminal time are determined to minimize the given cost and satisfy the state constraints. In the game problem, the two players interact via the system dynamics. Here, a strategy for each player, as well as a terminal time, are determined so that player A minimizes the cost and satisfies the state constraints while player B tries to prevent the success of player A. Dynamics, costs, and state...

ResiliNet: Failure-Resilient Inference in Distributed Neural Networks

Yousefpour, Ashkan
Nguyen, Brian Q.
Devic, Siddartha
Wang, Guanhua
Bayen, Alexandre
2020

Federated Learning aims to train distributed deep models without sharing the raw data with the centralized server. Similarly, in distributed inference of neural networks, by partitioning the network and distributing it across several physical nodes, activations and gradients are exchanged between physical nodes, rather than raw data. Nevertheless, when a neural network is partitioned and distributed among physical nodes, failure of physical nodes causes the failure of the neural units that are placed on those nodes, which results in a significant performance drop. Current approaches focus...

A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction

Chan, Victor
Gan, Qijian
Bayen, Alexandre
2020

Accurate and reliable prediction of traffic measurements plays a crucial role in the development of modern intelligent transportation systems. Due to more complex road geometries and the presence of signal control, arterial traffic prediction is a level above freeway traffic prediction. Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors. As a result, they often suffer large prediction errors, especially for long horizons. We fill...