Arena: A Scalable and Configurable Benchmark for Policy Learning

We believe current benchmarks for policy learning lack two important properties: 1 scalability and configurability. The growing literature on modeling policies as 2 graph neural networks calls for an object-based benchmark where the number 3 of objects can be arbitrarily scaled and the mechanics can be freely configured. 4 We introduce the Arena benchmark1, a scalable and configurable benchmark for 5 policy learning. Arena provides an object-based game-like environment where the 6 number of objects can be arbitrarily scaled and the mechanics can be configured 7 with a large degree of freedom. In this way, arena is designed to be an all-in-one 8 environment that uses scaling and configuration to smoothly interpolates multiple 9 dimensions of decision making that require different degrees of inductive bias. 10