Decentralized Reinforcement Learning for Multi-Target Search and Detection by a Team of Drones

• URL: http://arxiv.org/abs/2103.09520v1
• Date: Wed, 17 Mar 2021 09:04:47 GMT
• Title: Decentralized Reinforcement Learning for Multi-Target Search and Detection by a Team of Drones
• Authors: Roi Yehoshua, Juan Heredia-Juesas, Yushu Wu, Christopher Amato, Jose Martinez-Lorenzo
• Abstract summary: Targets search and detection encompasses a variety of decision problems such as coverage, surveillance, search, observing and pursuit-evasion. We develop a multi-agent deep reinforcement learning (MADRL) method to coordinate a group of aerial vehicles (drones) for the purpose of locating a set of static targets in an unknown area.
• Score: 12.055303570215335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Targets search and detection encompasses a variety of decision problems such as coverage, surveillance, search, observing and pursuit-evasion along with others. In this paper we develop a multi-agent deep reinforcement learning (MADRL) method to coordinate a group of aerial vehicles (drones) for the purpose of locating a set of static targets in an unknown area. To that end, we have designed a realistic drone simulator that replicates the dynamics and perturbations of a real experiment, including statistical inferences taken from experimental data for its modeling. Our reinforcement learning method, which utilized this simulator for training, was able to find near-optimal policies for the drones. In contrast to other state-of-the-art MADRL methods, our method is fully decentralized during both learning and execution, can handle high-dimensional and continuous observation spaces, and does not require tuning of additional hyperparameters.

Related papers

• Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance [0.0]
  It proposes a hybrid AI system, integrating deep reinforcement learning in a multi-agent centralized swarm architecture. The proposed system is tailored to perform surveillance of a specific area, searching and tracking ground targets, for security and law enforcement applications.
  arXiv  Detail & Related papers  (2025-01-15T08:46:20Z)
• Cooperative Search and Track of Rogue Drones using Multiagent Reinforcement Learning [8.775925011558995]
  This work considers the problem of intercepting rogue drones targeting sensitive critical infrastructure facilities. A holistic system that can reliably detect, track, and neutralize rogue drones is proposed.
  arXiv  Detail & Related papers  (2025-01-07T16:22:51Z)
• A Cross-Scene Benchmark for Open-World Drone Active Tracking [54.235808061746525]
  Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations. We propose a unified cross-scene cross-domain benchmark for open-world drone active tracking called DAT. We also propose a reinforcement learning-based drone tracking method called R-VAT.
  arXiv  Detail & Related papers  (2024-12-01T09:37:46Z)
• Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [60.05963742334746]
  We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers. Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy. We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
  arXiv  Detail & Related papers  (2024-09-12T11:50:06Z)
• Distributed multi-agent target search and tracking with Gaussian process and reinforcement learning [26.499110405106812]
  We propose a multi-agent reinforcement learning technique with target map building based on distributed process. We evaluate the performance and transferability of the trained policy in simulation and demonstrate the method on a swarm of micro unmanned aerial vehicles.
  arXiv  Detail & Related papers  (2023-08-29T01:53:14Z)
• Predictive Experience Replay for Continual Visual Control and Forecasting [62.06183102362871]
  We present a new continual learning approach for visual dynamics modeling and explore its efficacy in visual control and forecasting. We first propose the mixture world model that learns task-specific dynamics priors with a mixture of Gaussians, and then introduce a new training strategy to overcome catastrophic forgetting. Our model remarkably outperforms the naive combinations of existing continual learning and visual RL algorithms on DeepMind Control and Meta-World benchmarks with continual visual control tasks.
  arXiv  Detail & Related papers  (2023-03-12T05:08:03Z)
• Exploration via Planning for Information about the Optimal Trajectory [67.33886176127578]
  We develop a method that allows us to plan for exploration while taking the task and the current knowledge into account. We demonstrate that our method learns strong policies with 2x fewer samples than strong exploration baselines.
  arXiv  Detail & Related papers  (2022-10-06T20:28:55Z)
• Space Non-cooperative Object Active Tracking with Deep Reinforcement Learning [1.212848031108815]
  We propose an end-to-end active visual tracking method based on DQN algorithm, named as DRLAVT. It can guide the chasing spacecraft approach to arbitrary space non-cooperative target merely relied on color or RGBD images. It significantly outperforms position-based visual servoing baseline algorithm that adopts state-of-the-art 2D monocular tracker, SiamRPN.
  arXiv  Detail & Related papers  (2021-12-18T06:12:24Z)
• Multitask Adaptation by Retrospective Exploration with Learned World Models [77.34726150561087]
  We propose a meta-learned addressing model called RAMa that provides training samples for the MBRL agent taken from task-agnostic storage. The model is trained to maximize the expected agent's performance by selecting promising trajectories solving prior tasks from the storage.
  arXiv  Detail & Related papers  (2021-10-25T20:02:57Z)
• Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous [66.6895109554163]
  Underlying the human ability to align goals with other agents is their ability to predict the intentions of others and actively update their own plans. We propose hierarchical predictive planning (HPP), a model-based reinforcement learning method for decentralized multiagent rendezvous.
  arXiv  Detail & Related papers  (2020-03-15T19:49:20Z)

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.