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Progress in continual reinforcement learning has been limited due to severalbarriers to entry: missing code, high compute requirements, and a lack ofsuitable benchmarks. In this work, we present CORA, a platform for ContinualReinforcement Learning Agents that provides benchmarks, baselines, and metricsin a single code package. The benchmarks we provide are designed to evaluatedifferent aspects of the continual RL challenge, such as catastrophicforgetting, plasticity, ability to generalize, and sample-efficient learning.Three of the benchmarks utilize video game environments (Atari, Procgen,NetHack). The fourth benchmark, CHORES, consists of four different tasksequences in a visually realistic home simulator, drawn from a diverse set oftask and scene parameters. To compare continual RL methods on these benchmarks,we prepare three metrics in CORA: Continual Evaluation, Isolated Forgetting,and Zero-Shot Forward Transfer. Finally, CORA includes a set of performant,open-source baselines of existing algorithms for researchers to use and expandon. We release CORA and hope that the continual RL community can benefit fromour contributions, to accelerate the development of new continual RLalgorithms.

CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents