EnEnv 1.0: Energy Grid Environment for Multi-Agent Reinforcement Learning Benchmarking

Multi-agent reinforcement learning (MARL) offers prospects of efficient control in large distributed systems such as complex energy grids. The development of MARL algorithms is hampered by a scarcity of realistic benchmarks. In this paper, we introduce EnEnv 1.0 – a simulation benchmark for MARL in modern energy grids. EnEnv 1.0 is a set of environments […]