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Designing intelligent and robust autonomous navigation systems remains a great challenge in mobile robotics. Inverse reinforcement learning (IRL) offers an efficient learning technique from expert demonstrations to teach robots how to perform specific tasks without manually specifying the reward function. Most of existing IRL algorithms assume the expert policy to be optimal and deterministic, and are applied to experiments with relatively small-size state spaces. However, in autonomous navigation tasks, the state spaces are frequently large and demonstrations can hardly visit all the states. Meanwhile the expert policy may be non-optimal and stochastic. In this paper, we focus on IRL with large-scale and high-dimensional state spaces by introducing the neural network to generalize the expert’s behaviors to unvisited regions of the state space and an explicit policy representation is easily expressed by neural network, even for the stochastic expert policy. An efficient and convenient algorithm, Neural Inverse Reinforcement Learning (NIRL), is proposed. Experimental results on simulated autonomous navigation tasks show that a mobile robot using our approach can successfully navigate to the target position without colliding with unpredicted obstacles, largely reduce the learning time, and has a good generalization performance on undemonstrated states. Hence prove the robot intelligence of autonomous navigation transplanted from limited demonstrations to completely unknown tasks. [ABSTRACT FROM AUTHOR]

Highlights: [•] A general multi-agent based framework for virtual reality intelligent simulation system of vehicles is proposed. [•] Modeling and formal specifications of multi-agent traffic simulation are studied. [•] Dynamic features of the real world are realized in the simulation system. [•] Microscopic traffic behavior and vehicle control algorithm are verified by the intelligent simulation system. [Copyright &y& Elsevier]

The article presents a study which proposes a new hierarchical method for the flexible job-shop scheduling problem (FJSP). The FJSP is a nondeterministic polynomial-time hard since it is an extension of the job-shop scheduling problem. The first method proposed is based successively on a heuristic approach and a local search. The second one is based on a branch-and-bound algorithm.