MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding
October 25, 2019 Β· Declared Dead Β· π arXiv.org
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Authors
Wenbo Zhang, Osbert Bastani, Vijay Kumar
arXiv ID
1910.12639
Category
eess.SY: Systems & Control (EE)
Cross-listed
cs.AI,
cs.MA,
cs.RO
Citations
43
Venue
arXiv.org
Last Checked
6 months ago
Abstract
Reinforcement learning is a promising approach to learning control policies for performing complex multi-agent robotics tasks. However, a policy learned in simulation often fails to guarantee even simple safety properties such as obstacle avoidance. To ensure safety, we propose multi-agent model predictive shielding (MAMPS), an algorithm that provably guarantees safety for an arbitrary learned policy. In particular, it operates by using the learned policy as often as possible, but instead uses a backup policy in cases where it cannot guarantee the safety of the learned policy. Using a multi-agent simulation environment, we show how MAMPS can achieve good performance while ensuring safety.
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