Recurrent Auto-Encoders for Enhanced Deep Reinforcement Learning in Wilderness Search and Rescue Planning

• URL: http://arxiv.org/abs/2502.19356v1
• Date: Wed, 26 Feb 2025 17:54:47 GMT
• Title: Recurrent Auto-Encoders for Enhanced Deep Reinforcement Learning in Wilderness Search and Rescue Planning
• Authors: Jan-Hendrik Ewers, David Anderson, Douglas Thomson,
• Abstract summary: Wilderness search and rescue operations are often carried out over vast landscapes.<n>The problem is not one of complete coverage, but one of maximizing the information gathered in the limited time available.<n>We propose that a combination of a recurrent autoencoder and deep reinforcement learning is a more efficient solution to the search problem.
• Score: 0.294944680995069
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wilderness search and rescue operations are often carried out over vast landscapes. The search efforts, however, must be undertaken in minimum time to maximize the chance of survival of the victim. Whilst the advent of cheap multicopters in recent years has changed the way search operations are handled, it has not solved the challenges of the massive areas at hand. The problem therefore is not one of complete coverage, but one of maximizing the information gathered in the limited time available. In this work we propose that a combination of a recurrent autoencoder and deep reinforcement learning is a more efficient solution to the search problem than previous pure deep reinforcement learning or optimisation approaches. The autoencoder training paradigm efficiently maximizes the information throughput of the encoder into its latent space representation which deep reinforcement learning is primed to leverage. Without the overhead of independently solving the problem that the recurrent autoencoder is designed for, it is more efficient in learning the control task. We further implement three additional architectures for a comprehensive comparison of the main proposed architecture. Similarly, we apply both soft actor-critic and proximal policy optimisation to provide an insight into the performance of both in a highly non-linear and complex application with a large observation Results show that the proposed architecture is vastly superior to the benchmarks, with soft actor-critic achieving the best performance. This model further outperformed work from the literature whilst having below a fifth of the total learnable parameters and training in a quarter of the time.

Related papers

• REDSearcher: A Scalable and Cost-Efficient Framework for Long-Horizon Search Agents [40.38002661542917]
  REDSearcher is a unified framework that codesigns complex task synthesis, midtraining, and posttraining for scalable searchagent optimization.<n>We introduce toolaugmented queries to encourage proactive tool use rather than passive recall.<n>During midtraining, we strengthen core atomic capabilities knowledge, planning, and function calling.<n>We build a local simulated environment that enables rapid, lowcost algorithmic iteration for reinforcement learning experiments.
  arXiv  Detail & Related papers  (2026-02-15T17:04:46Z)
• An End-to-End Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drones [12.385878815004283]
  This study proposes a hierarchical Actor-Critic deep reinforcement learning framework for solving the Traveling Salesman Problem with Drones (TSP-D)<n>The architecture consists of two primary computation: a Transformer-inspired encoder and an efficient Minimal Gated Unit decoder.<n>The entire framework operates within an asynchronous advantage actor-critic paradigm.
  arXiv  Detail & Related papers  (2025-11-07T14:26:29Z)
• WebLeaper: Empowering Efficiency and Efficacy in WebAgent via Enabling Info-Rich Seeking [60.35109192765302]
  Information seeking is a core capability that enables autonomous reasoning and decision-making.<n>We propose WebLeaper, a framework for constructing high-coverage IS tasks and generating efficient solution trajectories.<n>Our method consistently achieves improvements in both effectiveness and efficiency over strong baselines.
  arXiv  Detail & Related papers  (2025-10-28T17:51:42Z)
• FlashResearch: Real-time Agent Orchestration for Efficient Deep Research [62.03819662340356]
  FlashResearch is a novel framework for efficient deep research.<n>It transforms sequential processing into parallel, runtime orchestration.<n>It can deliver up to a 5x speedup while maintaining comparable quality.
  arXiv  Detail & Related papers  (2025-10-02T00:15:39Z)
• DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search [53.27052683356095]
  We present DeepSearch, a framework that integrates Monte Carlo Tree Search directly into RLVR training.<n>In contrast to existing methods that rely on tree search only at inference, DeepSearch embeds structured search into the training loop.<n>Our contributions include: (1) a global frontier selection strategy that prioritizes promising nodes across the search tree, (2) selection with entropy-based guidance that identifies confident paths for supervision, and (3) adaptive replay buffer training with solution caching for efficiency.
  arXiv  Detail & Related papers  (2025-09-29T20:00:29Z)
• SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis [89.99161034065614]
  Retrieval-augmented generation (RAG) systems have advanced large language models (LLMs) in complex deep search scenarios.<n>Existing approaches face critical limitations that lack high-quality training trajectories and suffer from distributional mismatches.<n>This paper introduces SimpleDeepSearcher, a framework that bridges the gap through strategic data engineering rather than complex training paradigms.
  arXiv  Detail & Related papers  (2025-05-22T16:05:02Z)
• Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration [32.77845864484552]
  We introduce a new computational framework for RL with language models, in which the learner interacts with the model through a sampling oracle. We show that coverage, while not necessary for data efficiency, lower bounds the runtime of any algorithm in our framework. We introduce a new algorithm, SpannerSampling, which obtains optimal data efficiency and is computationally efficient whenever the pre-trained model enjoys sufficient coverage.
  arXiv  Detail & Related papers  (2025-03-10T15:31:42Z)
• A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks [81.2624272756733]
  In dense retrieval, deep encoders provide embeddings for both inputs and targets. We train a small parametric corrector network that adjusts stale cached target embeddings. Our approach matches state-of-the-art results even when no target embedding updates are made during training.
  arXiv  Detail & Related papers  (2024-09-03T13:29:13Z)
• Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopy [55.2480439325792]
  We introduce deep unrolled self-supervised learning, which alleviates the need for such data by training a sequence-specific, model-based autoencoder. Our proposed method exceeds the performance of its supervised counterparts.
  arXiv  Detail & Related papers  (2024-03-25T17:40:32Z)
• Masked Autoencoders Are Robust Neural Architecture Search Learners [14.965550562292476]
  We propose a novel NAS framework based on Masked Autoencoders (MAE) that eliminates the need for labeled data during the search process. By replacing the supervised learning objective with an image reconstruction task, our approach enables the robust discovery of network architectures.
  arXiv  Detail & Related papers  (2023-11-20T13:45:21Z)
• Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning [70.65666982566655]
  Permutation flow shop scheduling (PFSS) is widely used in manufacturing systems. We propose to train the model via expert-driven imitation learning, which accelerates convergence more stably and accurately. Our model's network parameters are reduced to only 37% of theirs, and the solution gap of our model towards the expert solutions decreases from 6.8% to 1.3% on average.
  arXiv  Detail & Related papers  (2022-10-31T09:46:26Z)
• Segmentation-guided Domain Adaptation for Efficient Depth Completion [3.441021278275805]
  We propose an efficient depth completion model based on a vgg05-like CNN architecture and a semi-supervised domain adaptation approach. In order to boost spatial coherence, we guide the learning process using segmentations as additional source of information. Our approach improves on previous efficient and low parameter state of the art approaches while having a noticeably lower computational footprint.
  arXiv  Detail & Related papers  (2022-10-14T13:01:25Z)
• Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories [72.15369769265398]
  Machine learning has emerged as a promising paradigm for branching. We propose retro branching; a simple yet effective approach to RL for branching. We outperform the current state-of-the-art RL branching algorithm by 3-5x and come within 20% of the best IL method's performance on MILPs with 500 constraints and 1000 variables.
  arXiv  Detail & Related papers  (2022-05-28T06:08:07Z)
• Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem [18.735056206844202]
  We show that we can construct a model capable of learning without supervision and inferences significantly faster than current autoregressive approaches. We also empirically evaluate the benefits of including optimal transport algorithms within deep learning models to enforce assignment constraints during end-to-end training.
  arXiv  Detail & Related papers  (2022-03-02T07:21:56Z)
• AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning [72.99415402575886]
  Outlier detection is an important data mining task with numerous practical applications. We propose AutoOD, an automated outlier detection framework, which aims to search for an optimal neural network model. Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoOD achieves the best performance.
  arXiv  Detail & Related papers  (2020-06-19T18:57:51Z)
• Forgetful Experience Replay in Hierarchical Reinforcement Learning from Demonstrations [55.41644538483948]
  In this paper, we propose a combination of approaches that allow the agent to use low-quality demonstrations in complex vision-based environments. Our proposed goal-oriented structuring of replay buffer allows the agent to automatically highlight sub-goals for solving complex hierarchical tasks in demonstrations. The solution based on our algorithm beats all the solutions for the famous MineRL competition and allows the agent to mine a diamond in the Minecraft environment.
  arXiv  Detail & Related papers  (2020-06-17T15:38:40Z)

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.