Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsistic" approaches, which do not consider interactions between agents. Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios. Each scenario pairs a physical environment (a "substrate") with a reference set of co-players (a "background population"), to create a social situation with substantial interdependence between the individuals involved. For instance, some scenarios were inspired by institutional-economics-based accounts of natural resource management and public-good-provision dilemmas. Others were inspired by considerations from evolutionary biology, game theory, and artificial life. Melting Pot aims to cover a maximally diverse set of interdependencies and incentives. It includes the commonly-studied extreme cases of perfectly-competitive (zero-sum) motivations and perfectly-cooperative (shared-reward) motivations, but does not stop with them. As in real-life, a clear majority of scenarios in Melting Pot have mixed incentives. They are neither purely competitive nor purely cooperative and thus demand successful agents be able to navigate the resulting ambiguity. Here we describe Melting Pot 2.0, which revises and expands on Melting Pot. We also introduce support for scenarios with asymmetric roles, and explain how to integrate them into the evaluation protocol. This report also contains: (1) details of all substrates and scenarios; (2) a complete description of all baseline algorithms and results. Our intention is for it to serve as a reference for researchers using Melting Pot 2.0.

合作匪徒问题越来越多地成为其在大规模决策中的应用。然而，对此问题的大多数研究专注于具有完美通信的环境，而在大多数现实世界分布式设置中，通信通常是随机网络，具有任意损坏和延迟。在本文中，我们在三个典型的真实沟通场景下研究了合作匪徒学习，即（a）通过随机时变网络的消息传递，（b）通过随机延迟的网络瞬时奖励共享（c ）通过对冲损坏的奖励来传递消息，包括拜占庭式沟通。对于每个环境中的每一个，我们提出了实现竞争性能的分散算法，以及在发生的群体后悔的近乎最佳保证。此外，在具有完美通信的环境中，我们提出了一种改进的延迟更新算法，其优于各种网络拓扑的现有最先进的算法。最后，我们在集团后悔呈现紧密的网络依赖性最低限度。我们所提出的算法很简单，以实现和获得竞争性的经验性能。