Multiple Benchmarks for MARL

该数据集包含多个基准测试，旨在评估逻辑基础奖励塑造在多代理强化学习场景中的性能表现。此外，数据集还包含了针对基准线的比较实验，这些实验对代理进行了平均处理，并采用了特定奖励结构的归一化回报。该数据集覆盖了多种场景，其中包括不同数量的代理，其任务是研究基于逻辑奖励塑造的多代理强化学习。

This dataset comprises multiple benchmark tests designed to evaluate the performance of logic-based reward shaping in multi-agent reinforcement learning scenarios. Additionally, the dataset includes comparative experiments against baseline approaches, in which the outcomes are averaged across agents and normalized returns with specific reward structures are employed. This dataset covers a diverse range of scenarios involving varying numbers of agents, which are tailored for researching multi-agent reinforcement learning with logic-based reward shaping.