Multi-robot inverse reinforcement learning under occlusion with interactions

By Kenneth D. Bogert, Prashant Doshi. 2014

42 citations recorded by the source corpus.

We consider the problem of learning the behavior of multiple mobile robots executing fixed trajectories in a common space and possibly interacting with each other in their execution. The mobile robots are observed by a subject robot from a vantage point from which it can observe a portion of their trajectories only. This problem exhibits wide-ranging applications and the specific application we consider here is that of the subject robot who desires to penetrate a simple perimeter patrol by two interacting robots and reach a goal location. Our approach extends single-agent inverse reinforcement learning (IRL) to a multi-robot setting and partial observability, and models the interaction between the mobile robots as equilibrium behavior. IRL provides weights over the features of the robots' reward functions, thereby allowing us to learn their preferences. Subsequently, we derive a Markov decision process based policy for each other robot. We extend a predominant IRL technique and empirically evaluate its performance in our application setting. We show that our approach in the application setting results in significant improvement in the subject's ability to predict the patroller positions at different points in time with a corresponding increase in its successful penetration rate.

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Citations in the corpus

Learning Multi-Arm Manipulation Through Collaborative Teleoperation (2020, methodology): “Multi-Agent Imitation Learning: Most work in MultiAgent Imitation Learning [4, 20, 35, 36] focuses on the paradigm of Inverse Reinforcement Learning [1, 2], in which multi-agent demonstrations are used to infer a reward function, and the reward function is optimized via Reinforcement”
An Algorithmic Perspective on Imitation Learning (2018, methodology): “, 2012, Bogert and Doshi, 2014, 2015, Bogert et al., 2016]. Noisy sensors are a common source of partial observability. To forecast human activities from noisy images, [Kitani et al., 2012] extends maximum entropy IRL [Ziebart et al., 2008] into domains where the learner only partially observes expe”
Multi-Agent Generative Adversarial Imitation Learning (2018, background): “The most common case is fully cooperative agents, where the challenges mainly lie 260 in other factors, such as unknown role assignments [36], scalability to swarm systems [37] and 261 agents with partial observations [38].”