Smart Containers Bidding Data

该数据集包含了用于调整强化学习算法超参数的数据，这些参数适用于在半合作多代理环境中智能集装箱的竞价策略。具体来说，数据集涵盖了影响竞价决策的作业属性和系统特性，包括学习速率、偏差权重以及竞价算法中的标准差等参数。此外，该数据集是在一个模拟环境中进行评估的，该环境包含了100万时间步长的数据。其任务是对智能集装箱的竞价策略进行优化。

This dataset provides data for tuning the hyperparameters of reinforcement learning algorithms, which are applicable to the bidding strategies of intelligent containers in semi-cooperative multi-agent environments. Specifically, it encompasses operational attributes and system characteristics that influence bidding decision-making, including parameters such as learning rate, bias weight, and standard deviation within the bidding algorithm. Furthermore, this dataset was evaluated in a simulated environment containing 1 million time-step data points. Its core objective is to optimize the bidding strategies of intelligent containers.