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Robust Inverse Planning Approaches for Policy Estimation of Semi-autonomous Agents

Most of existing coordination techniques for autonomous agents assume the knowledge or the estimation of the other agents’ policy. However, this assumption is not valid in semi-autonomous agents because an external entity can take the control and modify the behavior of the agent. We face this problem in applications where an operator can take the control of the system (Robot/UAV). Many human factors may affect this behavior, such as stress, hesitations and preferences. Estimating the policy in such contexts is a difficult problem. Many existing algorithms using Inverse Reinforcement learning or imitation have been developed. However most of them have weak performance when non-optimal policy is followed. In this paper, we investigate techniques for estimating the followed policies of semi-autonomous agents that could be nonoptimal due to critical situations We extend some prediction methods and algorithms based on Factored MDPs and Inverse Reinforcement Learning to improve their stability and their efficiency during the execution of a mission. Then, we develop various experiments showing the performance on efficiency and stability of our approach in different conditions and comparing with it.