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Sachin Katti

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4 papers

2 author rows

NeurIPS Conference 2021 Conference Paper

Data Sharing and Compression for Cooperative Networked Control

Jiangnan Cheng
Marco Pavone
Sachin Katti
Sandeep Chinchali
Ao Tang

Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power generation. Typically, forecasts are designed without knowledge of a downstream controller's task objective, and thus simply optimize for mean prediction error. However, such task-agnostic representations are often too large to stream over a communication network and do not emphasize salient temporal features for cooperative control. This paper presents a solution to learn succinct, highly-compressed forecasts that are co-designed with a modular controller's task objective. Our simulations with real cellular, Internet-of-Things (IoT), and electricity load data show we can improve a model predictive controller's performance by at least 25% while transmitting 80% less data than the competing method. Further, we present theoretical compression results for a networked variant of the classical linear quadratic regulator (LQR) control problem.

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ICLR Conference 2020 Conference Paper

Multi-agent Reinforcement Learning for Networked System Control

Tianshu Chu
Sandeep Chinchali
Sachin Katti

This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors. We formulate such a networked MARL (NMARL) problem as a spatiotemporal Markov decision process and introduce a spatial discount factor to stabilize the training of each local agent. Further, we propose a new differentiable communication protocol, called NeurComm, to reduce information loss and non-stationarity in NMARL. Based on experiments in realistic NMARL scenarios of adaptive traffic signal control and cooperative adaptive cruise control, an appropriate spatial discount factor effectively enhances the learning curves of non-communicative MARL algorithms, while NeurComm outperforms existing communication protocols in both learning efficiency and control performance.

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RLDM Conference 2019 Conference Abstract

Reinforcement Learning for Network Offloading in Cloud Robotics

Sandeep Chinchali
Apoorva Sharma
Amine Elhafsi
Daniel Kang
Evgenya Perga-
Eyal Cidon
Sachin Katti

We apply deep reinforcement learning to a central decision-making problem in robotics - when should a robot use its on-board compute model or, in cases of local uncertainty, query a compute-intensive model in “the cloud”? Today’s robotic systems are increasingly turning to computationally expensive mod- els such as deep neural networks (DNNs) for tasks like object detection, perception and planning. However, resource-constrained robots, like low-power drones, often have insufficient on-board compute resources or power reserves to scalably run the most accurate, state-of-the art neural network compute models. Cloud robotics allows mobile robots to offload compute to centralized servers if they are uncertain locally or want to run more accurate, compute-intensive models. However, cloud robotics comes with a key, often un- derstated cost: communicating with the cloud over congested wireless networks may result in latency and increase network congestion. In fact, sending high data-rate video or LIDAR from multiple robots over congested networks can lead to prohibitive delay for real-time applications. We formulate a novel Robot Offloading Problem — how and when should robots offload sensing tasks, especially if they are uncertain, to improve accuracy while minimizing the cost of cloud communication? We formulate offloading as a sequential decision making problem for robots, and propose a solution using deep reinforcement learning. In both simulations and practical hardware experiments using state-of-the art vision DNNs, our offloading strategy improves vision task performance by between 1. 3-2. 6x of benchmark offloading strategies. We conclude by showing how cloud offloading has an inherent exploration vs. exploitation trade-off since a robot must balance use of a known local model (exploitation) with learning context-dependent utility of the cloud (exploration). Accordingly, we discuss how our model is widely applicable beyond cloud robotics.

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AAAI Conference 2018 Conference Paper

Cellular Network Traffic Scheduling With Deep Reinforcement Learning

Sandeep Chinchali
Pan Hu
Tianshu Chu
Manu Sharma
Manu Bansal
Rakesh Misra
Marco Pavone
Sachin Katti

Modern mobile networks are facing unprecedented growth in demand due to a new class of trafﬁc from Internet of Things (IoT) devices such as smart wearables and autonomous cars. Future networks must schedule delay-tolerant software updates, data backup, and other transfers from IoT devices while maintaining strict service guarantees for conventional realtime applications such as voice-calling and video. This problem is extremely challenging because conventional trafﬁc is highly dynamic across space and time, so its performance is signiﬁcantly impacted if all IoT trafﬁc is scheduled immediately when it originates. In this paper, we present a reinforcement learning (RL) based scheduler that can dynamically adapt to trafﬁc variation, and to various reward functions set by network operators, to optimally schedule IoT trafﬁc. Using 4 weeks of real network data from downtown Melbourne, Australia spanning diverse trafﬁc patterns, we demonstrate that our RL scheduler can enable mobile networks to carry 14. 7% more data with minimal impact on existing trafﬁc, and outperforms heuristic schedulers by more than 2×. Our work is a valuable step towards designing autonomous, “selfdriving” networks that learn to manage themselves from past data.

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