Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamics

• URL: http://arxiv.org/abs/2312.11834v4
• Date: Mon, 07 Oct 2024 10:28:24 GMT
• Title: Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamics
• Authors: Hisato Komatsu,
• Abstract summary: This study considered the roads on a grid-world environment, and implemented pedestrians as MARL agents using an echo-state network and the least squares policy iteration method. Under this environment, the ability of these agents to learn to move forward by avoiding other agents was investigated.
• Score: 0.0
• License:
• Abstract: In recent years, simulations of pedestrians using the multi-agent reinforcement learning (MARL) have been studied. This study considered the roads on a grid-world environment, and implemented pedestrians as MARL agents using an echo-state network and the least squares policy iteration method. Under this environment, the ability of these agents to learn to move forward by avoiding other agents was investigated. Specifically, we considered two types of tasks: the choice between a narrow direct route and a broad detour, and the bidirectional pedestrian flow in a corridor. The simulations results indicated that the learning was successful when the density of the agents was not that high.

Related papers

• iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning [57.24340061741223]
  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.
  arXiv  Detail & Related papers  (2023-06-09T20:12:02Z)
• Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation [45.09881984441893]
  We propose Symphony, which greatly improves realism by combining conventional policies with a parallel beam search. Symphony addresses this issue with a hierarchical approach, factoring agent behaviour into goal generation and goal conditioning. Experiments confirm that Symphony agents learn more realistic and diverse behaviour than several baselines.
  arXiv  Detail & Related papers  (2022-05-06T13:21:40Z)
• Jointly Learning Agent and Lane Information for Multimodal Trajectory Prediction [9.602388354570111]
  We propose a staged network that Jointly learning Agent and Lane information for Multimodal Trajectory Prediction. Experiments conducted on the public Argoverse dataset demonstrate that JAL-MTP significantly outperforms the existing models in both quantitative and qualitative.
  arXiv  Detail & Related papers  (2021-11-26T08:02:06Z)
• Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments [62.997667081978825]
  We consider the problem of multi-agent navigation in partially observable grid environments. We suggest utilizing the reinforcement learning approach when the agents, first, learn the policies that map observations to actions and then follow these policies to reach their goals.
  arXiv  Detail & Related papers  (2021-08-13T09:44:47Z)
• Cooperative Exploration for Multi-Agent Deep Reinforcement Learning [127.4746863307944]
  We propose cooperative multi-agent exploration (CMAE) for deep reinforcement learning. The goal is selected from multiple projected state spaces via a normalized entropy-based technique. We demonstrate that CMAE consistently outperforms baselines on various tasks.
  arXiv  Detail & Related papers  (2021-07-23T20:06:32Z)
• Effects of Smart Traffic Signal Control on Air Quality [0.0]
  Multi-agent deep reinforcement learning (MARL) has been studied experimentally in traffic systems. A recently developed multi-agent variant of the well-established advantage actor-critic (A2C) algorithm, called MA2C, exploits the promising idea of some communication among the agents. In this view, the agents share their strategies with other neighbor agents, thereby stabilizing the learning process even when the agents grow in number and variety.
  arXiv  Detail & Related papers  (2021-07-06T02:48:42Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Language-guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning [66.9937776799536]
  The emerging vision-and-language navigation (VLN) problem aims at learning to navigate an agent to the target location in unseen photo-realistic environments. The main challenges of VLN arise mainly from two aspects: first, the agent needs to attend to the meaningful paragraphs of the language instruction corresponding to the dynamically-varying visual environments. We propose a cross-modal grounding module to equip the agent with a better ability to track the correspondence between the textual and visual modalities.
  arXiv  Detail & Related papers  (2020-11-22T09:13:46Z)
• Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment [11.093194714316434]
  We develop a Markov routing game (MRG) in which each agent learns and updates her own en-route path choice policy. We show that the routing behavior of intelligent agents is shown to converge to the classical notion of predictive dynamic user equilibrium.
  arXiv  Detail & Related papers  (2020-11-22T02:31:14Z)
• Multi-Agent Routing Value Iteration Network [88.38796921838203]
  We propose a graph neural network based model that is able to perform multi-agent routing based on learned value in a sparsely connected graph. We show that our model trained with only two agents on graphs with a maximum of 25 nodes can easily generalize to situations with more agents and/or nodes.
  arXiv  Detail & Related papers  (2020-07-09T22:16:45Z)
• Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding [46.52703817997932]
  Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately anticipate the behaviors of the surrounding vehicles and pedestrians. We propose a model that synthesizes multiple input signals from the multimodal world. We show a significant performance improvement over previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-03-06T13:59:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.