Worked on the pinterest/ray repository to address a critical issue in RLLib’s MultiAgentEnvRunner, focusing on accurate metric aggregation for multi-agent reinforcement learning experiments. The work involved fixing a bug where episode return metrics were incorrectly accumulated when multiple agents shared the same module ID, ensuring that module returns now correctly reflect the sum of agent returns. Expanded test coverage using pytest to prevent future regressions and added a dedicated regression test to verify metric accuracy. Leveraged Python and deep knowledge of RLLib internals to deliver more reliable performance evaluation and streamlined debugging for complex multi-agent machine learning environments.