Emergence of Scenario-Appropriate Collaborative Behaviors for Teams of Robotic Bodyguards
By Hassam Sheikh, Ladislau Bölöni. 2018
3 citations recorded by the source corpus.
We are considering the problem of controlling a team of robotic bodyguards protecting a VIP from physical assault in the presence of neutral and/or adversarial bystanders in a variety of scenarios. This problem is challenging due to the large number of active entities with different agendas and dynamic movement patterns, the need of cooperation between the robots as well as the requirement to take into consideration criteria such as social norms in addition to the main goal of VIP safety. We show how a multi-agent reinforcement learning approach can evolve behavior policies that outperform hand-engineered approaches. Furthermore, we propose a novel multi-agent reinforcement learning algorithm inspired by universal value function approximators that can learn policies that exhibit appropriate, distinct behavior in environments with different requirements.
Subfields: Artificial Intelligence, Safety Research, Social Psychology
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In Artificial Intelligence
- Guo-Jun Qi
- Mubarak Shah
- Muhammad Abdullah Jamal
- Luc Van Gool
- Chen Sun
- Andrew Zisserman
- Cordelia Schmid
- Limin Wang
In Social Psychology
Citations in the corpus
- Task Agnostic Meta-Learning for Few-Shot Learning (2018, background): “A reinforcement learning task T i is defined as Markov Decision Process that consists of a state space S , an action spaceA, the reward functionR, and state-transition probabilities qi(xt+1|xt, at) where at is the action at time step t [26, 27].”