Blame attribution isoneofthekeyaspects ofaccountable decision making, asit provides means to quantify the responsibility of an agent for a decision making outcome. Inthis paper,we study blame attribution inthe contextof cooperative multi-agent sequential decision making.

Recently, such difficulty in multi-agent learning has been eased owing to the introduction ofcentralized training for decentralized execution(CTDE) [11, 45], which allows agents to access the global information andopponents' actions during thetraining phase.

This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle ("AEC") games model. PettingZoo is a library of diverse sets of multi-agent environments with a universal, elegant Python API. PettingZoo was developed with the goal of accelerating research in Multi-Agent Reinforcement Learning ("MARL "), by making work more interchangeable, accessible and reproducible akin to what OpenAI's Gym library did for single-agent reinforcement

An increasingly common setting in machine learning involves multiple parties, each with their own data, who want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents to make better predictions than they would individually, but may not be willing to release labeled data or model parameters.

An increasingly common setting in machine learning involves multiple parties, each with their own data, who want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents to make better predictions than they would individually, but may not be willing to release labeled data or model parameters.