🤖 AI Summary
This work proposes a decentralized aggregation method for multi-agent systems equipped only with limited range and bearing sensing capabilities, leveraging image-based observations as input. Local perceptual information is encoded into structured images, from which spatial features are extracted using convolutional neural networks, and a scalable control policy is trained via deep reinforcement learning. By representing agent perception as images—an approach novel in multi-agent settings—the method circumvents the limitations of handcrafted feature engineering or fixed vector representations, significantly enhancing policy generalization across varying group sizes and initial configurations. Experimental results demonstrate that the proposed approach achieves high success rates in diverse and complex scenarios, exhibits convergence speeds comparable to VariAntNet, and, in several challenging setups, emerges as the sole viable solution, substantially outperforming conventional analytical methods.
📝 Abstract
This study highlights the potential of image-based reinforcement learning methods for addressing swarm-related tasks. In multi-agent reinforcement learning, effective policy learning depends on how agents sense, interpret, and process inputs. Traditional approaches often rely on handcrafted feature extraction or raw vector-based representations, which limit the scalability and efficiency of learned policies concerning input order and size. In this work we propose an image-based reinforcement learning method for decentralized control of a multi-agent system, where observations are encoded as structured visual inputs that can be processed by Neural Networks, extracting its spatial features and producing novel decentralized motion control rules. We evaluate our approach on a multi-agent convergence task of agents with limited-range and bearing-only sensing that aim to keep the swarm cohesive during the aggregation. The algorithm's performance is evaluated against two benchmarks: an analytical solution proposed by Bellaiche and Bruckstein, which ensures convergence but progresses slowly, and VariAntNet, a neural network-based framework that converges much faster but shows medium success rates in hard constellations. Our method achieves high convergence, with a pace nearly matching that of VariAntNet. In some scenarios, it serves as the only practical alternative.