no code implementations • 16 Mar 2022 • Lukas M. Schmidt, Sebastian Rietsch, Axel Plinge, Bjoern M. Eskofier, Christopher Mutschler
This paper proposes SafeDQN, which allows to make the behavior of autonomous vehicles safe and interpretable while still being efficient.
no code implementations • 15 Mar 2022 • Lukas M. Schmidt, Johanna Brosig, Axel Plinge, Bjoern M. Eskofier, Christopher Mutschler
Multi-Agent Reinforcement Learning (MARL) is a research field that aims to find optimal solutions for multiple agents that interact with each other.