Handling Realistic Noise in Multi-Agent Systems with Self-Supervised Learning and Curiosity

Márton Szemenyei, Patrik Reizinger
2021 Journal of Artificial Intelligence and Soft Computing Research  
1Most reinforcement learning benchmarks – especially in multi-agent tasks – do not go beyond observations with simple noise; nonetheless, real scenarios induce more elaborate vision pipeline failures: false sightings, misclassifications or occlusion. In this work, we propose a lightweight, 2D environment for robot soccer and autonomous driving that can emulate the above discrepancies. Besides establishing a benchmark for accessible multi-agent reinforcement learning research, our work addresses
more » ... the challenges the simulator imposes. For handling realistic noise, we use self-supervised learning to enhance scene reconstruction and extend curiosity-driven learning to model longer horizons. Our extensive experiments show that the proposed methods achieve state-of-the-art performance, compared against actor-critic methods, ICM, and PPO.
doi:10.2478/jaiscr-2022-0009 fatcat:7fc6pnhnj5ac7azxwteysnetmy