A.1 More Information About The Continuous Environment We provide a detailed description of the continuous environments with constrained settings: Let's consider an optimization problem in the form of: minimize α After analyzing Table C.1 and Figure C.1, it is evident that the B2CL, MEICRL, and InfoGAIL-ICRL Although MMICRL-LD shows a notable improvement, its performance remains mediocre in environments involving three types of agents. Table C.2 presents the mean std results of all algorithms in Mujoco. Figure C.2 depicts the distribution of x-coordinate values Half-Cheetah, Blocked Swimmer, and Blocked Walker environments. It demonstrates the algorithm's capacity to infer and restore incorrect We employ "/" to separate the results for various We present the mean std results calculated over 20 runs for each random seed.Method Setting 1 Setting 2 Setting 3 Setting 4 Feasible Cumulative Rewards B2CL 0.24 0 .40 Figure C.1: The feasible cumulative rewards (left two columns of the first three rows and second-to-last row) and constraint violation rate (right two columns of the first three rows and last row). The first row showcases the expert demonstration, followed by the results of B2CL, MEICRL, InfoGAIL-ICRL, MMICRL-LD, and MMICRL algorithms.

Wang et al. [ 2022 ] considered an online resource allocation problem where the

Suppose a revenue-maximizing recommendation algorithm concludes from past data that more revenue is generated by showing the ad to Group A compared to Group B. In that case, the ad-serving algorithm will eventually end up showing that ad exclusively to Group A

In fact, the interaction of these two aspects requires addressing the fact that each agent's own safety constraint requires information from all others.

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