9,327 research outputs found
iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning
Navigating safely and efficiently in dense and heterogeneous traffic
scenarios is challenging for autonomous vehicles (AVs) due to their inability
to infer the behaviors or intentions of nearby drivers. In this work, we
introduce a distributed multi-agent reinforcement learning (MARL) algorithm
that can predict trajectories and intents in dense and heterogeneous traffic
scenarios. Our approach for intent-aware planning, iPLAN, allows agents to
infer nearby drivers' intents solely from their local observations. We model
two distinct incentives for agents' strategies: Behavioral Incentive for
high-level decision-making based on their driving behavior or personality and
Instant Incentive for motion planning for collision avoidance based on the
current traffic state. Our approach enables agents to infer their opponents'
behavior incentives and integrate this inferred information into their
decision-making and motion-planning processes. We perform experiments on two
simulation environments, Non-Cooperative Navigation and Heterogeneous Highway.
In Heterogeneous Highway, results show that, compared with centralized training
decentralized execution (CTDE) MARL baselines such as QMIX and MAPPO, our
method yields a 4.3% and 38.4% higher episodic reward in mild and chaotic
traffic, with 48.1% higher success rate and 80.6% longer survival time in
chaotic traffic. We also compare with a decentralized training decentralized
execution (DTDE) baseline IPPO and demonstrate a higher episodic reward of
12.7% and 6.3% in mild traffic and chaotic traffic, 25.3% higher success rate,
and 13.7% longer survival time
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
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