MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control
Traffic signal control aims to coordinate traffic signals across intersections to improve the traffic efficiency of a district or a city. Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated promising performance where each traffic signal is regarded as an agent. However, there are still several challenges that may limit its large-scale application in the real world. To make the policy learned from a training scenario generalizable to new unseen scenarios, a novel Meta Variationally Intrinsic Motivated (MetaVIM) RL method is proposed to learn the decentralized policy for each intersection that considers neighbor information in a latent way. Specifically, we formulate the policy learning as a meta-learning problem over a set of related tasks, where each task corresponds to traffic signal control at an intersection whose neighbors are regarded as the unobserved part of the state. Then, a learned latent variable is introduced to represent the task's specific information and is further brought into the policy for learning. In addition, to make the policy learning stable, a novel intrinsic reward is designed to encourage each agent's received rewards and observation transition to be predictable only conditioned on its own history. Extensive experiments conducted on CityFlow demonstrate that the proposed method substantially outperforms existing approaches and shows superior generalizability.
Code (3)
Tasks
Deep Reinforcement LearningMeta-LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Traffic Signal ControlMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Intrinsically Guided Exploration in Meta Reinforcement Learning
Deep reinforcement learning algorithms generally require large amounts of data to solve a single task. Meta reinforcement learning (meta-RL) agents learn to adapt to novel unseen tasks with high sample efficiency by extr…
Deep Reinforcement LearningEfficient ExplorationMeta Reinforcement LearningMuJoCo+3Learning with AMIGo: Adversarially Motivated Intrinsic Goals
A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards. In contrast to current RL methods, humans are able to learn new skills with little or no reward by using…
Meta-LearningReinforcement Learning (RL)Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration
Intrinsic rewards can improve exploration in reinforcement learning, but the exploration process may suffer from instability caused by non-stationary reward shaping and strong dependency on hyperparameters. In this work,…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Intrinsically-Motivated Reinforcement Learning: A Brief Introduction
Reinforcement learning (RL) is one of the three basic paradigms of machine learning. It has demonstrated impressive performance in many complex tasks like Go and StarCraft, which is increasingly involved in smart manufac…
Autonomous Drivingreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey
Building autonomous machines that can explore open-ended environments, discover possible interactions and build repertoires of skills is a general objective of artificial intelligence. Developmental approaches argue that…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)