DREAM: Decentralized Reinforcement Learning for Exploration and Efficient Energy Management in Multi-Robot Systems

• URL: http://arxiv.org/abs/2309.17433v1
• Date: Fri, 29 Sep 2023 17:43:41 GMT
• Title: DREAM: Decentralized Reinforcement Learning for Exploration and Efficient Energy Management in Multi-Robot Systems
• Authors: Dipam Patel, Phu Pham, Kshitij Tiwari and Aniket Bera
• Abstract summary: Resource-constrained robots often suffer from energy inefficiencies, underutilized computational abilities due to inadequate task allocation, and a lack of robustness in dynamic environments. This paper introduces DREAM - Decentralized Reinforcement Learning for Exploration and Efficient Energy Management in Multi-Robot Systems.
• Score: 14.266876062352424
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Resource-constrained robots often suffer from energy inefficiencies, underutilized computational abilities due to inadequate task allocation, and a lack of robustness in dynamic environments, all of which strongly affect their performance. This paper introduces DREAM - Decentralized Reinforcement Learning for Exploration and Efficient Energy Management in Multi-Robot Systems, a comprehensive framework that optimizes the allocation of resources for efficient exploration. It advances beyond conventional heuristic-based task planning as observed conventionally. The framework incorporates Operational Range Estimation using Reinforcement Learning to perform exploration and obstacle avoidance in unfamiliar terrains. DREAM further introduces an Energy Consumption Model for goal allocation, thereby ensuring mission completion under constrained resources using a Graph Neural Network. This approach also ensures that the entire Multi-Robot System can survive for an extended period of time for further missions compared to the conventional approach of randomly allocating goals, which compromises one or more agents. Our approach adapts to prioritizing agents in real-time, showcasing remarkable resilience against dynamic environments. This robust solution was evaluated in various simulated environments, demonstrating adaptability and applicability across diverse scenarios. We observed a substantial improvement of about 25% over the baseline method, leading the way for future research in resource-constrained robotics.

Related papers

• Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning [53.3760591018817]
  We propose a new benchmarking environment for aquatic navigation using recent advances in the integration between game engines and Deep Reinforcement Learning. Specifically, we focus on PPO, one of the most widely accepted algorithms, and we propose advanced training techniques. Our empirical evaluation shows that a well-designed combination of these ingredients can achieve promising results.
  arXiv  Detail & Related papers  (2024-05-30T23:20:23Z)
• Diffusion-Reinforcement Learning Hierarchical Motion Planning in Adversarial Multi-agent Games [6.532258098619471]
  We focus on a motion planning task for an evasive target in a partially observable multi-agent adversarial pursuit-evasion games (PEG) These pursuit-evasion problems are relevant to various applications, such as search and rescue operations and surveillance robots. We propose a hierarchical architecture that integrates a high-level diffusion model to plan global paths responsive to environment data.
  arXiv  Detail & Related papers  (2024-03-16T03:53:55Z)
• From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban Search and Rescue [46.377510400989536]
  We present a novel hybrid algorithm for efficient multi-robot exploration in unknown environments with limited communication and no global positioning information. We redefine the local best and global best positions to suit scenarios without continuous target information. The presented work holds promise for enhancing multi-robot exploration in scenarios with limited information and communication capabilities.
  arXiv  Detail & Related papers  (2023-11-28T17:05:25Z)
• Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning [58.3994826169858]
  We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning. Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy. Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
  arXiv  Detail & Related papers  (2023-10-23T17:50:08Z)
• Bridging Active Exploration and Uncertainty-Aware Deployment Using Probabilistic Ensemble Neural Network Dynamics [11.946807588018595]
  This paper presents a unified model-based reinforcement learning framework that bridges active exploration and uncertainty-aware deployment. The two opposing tasks of exploration and deployment are optimized through state-of-the-art sampling-based MPC. We conduct experiments on both autonomous vehicles and wheeled robots, showing promising results for both exploration and deployment.
  arXiv  Detail & Related papers  (2023-05-20T17:20:12Z)
• Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning [70.70104870417784]
  Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems. In practice, real-world robotic RL typically requires time consuming data collection and frequent human intervention to reset the environment. In this work, we study how these challenges can be tackled by effective utilization of diverse offline datasets collected from previously seen tasks.
  arXiv  Detail & Related papers  (2022-07-11T08:31:22Z)
• Online reinforcement learning with sparse rewards through an active inference capsule [62.997667081978825]
  This paper introduces an active inference agent which minimizes the novel free energy of the expected future. Our model is capable of solving sparse-reward problems with a very high sample efficiency. We also introduce a novel method for approximating the prior model from the reward function, which simplifies the expression of complex objectives.
  arXiv  Detail & Related papers  (2021-06-04T10:03:36Z)
• Autonomous sPOMDP Environment Modeling With Partial Model Exploitation [0.0]
  We present a novel state space exploration algorithm by extending the original surprise-based partially-observable Markov Decision Processes (sPOMDP) We show the proposed model significantly increases efficiency and scalability of the original sPOMDP learning techniques with a range of 31-63% gain in training speed. Our results pave the way for extending sPOMDP solutions to a broader set of environments.
  arXiv  Detail & Related papers  (2020-12-22T17:48:32Z)
• Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles [73.15950858151594]
  This paper presents Latent Optimistic Value Exploration (LOVE), a strategy that enables deep exploration through optimism in the face of uncertain long-term rewards. We combine latent world models with value function estimation to predict infinite-horizon returns and recover associated uncertainty via ensembling. We apply LOVE to visual robot control tasks in continuous action spaces and demonstrate on average more than 20% improved sample efficiency in comparison to state-of-the-art and other exploration objectives.
  arXiv  Detail & Related papers  (2020-10-27T22:06:57Z)
• Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning [10.835960004409708]
  We propose a novel Deep Reinforcement Learning approach to solve the dynamic problem in mining. We first develop an event-based mining simulator with parameters calibrated in real mines. Then we propose an experience-sharing Deep Q Network with a novel abstract state/action representation to learn memories from heterogeneous agents.
  arXiv  Detail & Related papers  (2020-08-24T21:29:56Z)

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.