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Handling Realistic Noise in Multi-Agent Systems with Self-Supervised Learning and Curiosity
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 addressesdoi:10.2478/jaiscr-2022-0009 fatcat:7fc6pnhnj5ac7azxwteysnetmy