2022-11-28-powers22b.md

title	abstract	video	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents	Progress in continual reinforcement learning has been limited due to several barriers to entry: missing code, high compute requirements, and a lack of suitable benchmarks. In this work, we present CORA, a platform for Continual Reinforcement Learning Agents that provides benchmarks, baselines, and metrics in a single code package. The benchmarks we provide are designed to evaluate different aspects of the continual RL challenge, such as catastrophic forgetting, 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 task sequences in a visually realistic home simulator, drawn from a diverse set of task 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 expand on. We release CORA and hope that the continual RL community can benefit from our contributions, to accelerate the development of new continual RL algorithms.	https://youtu.be/R3BkNbPIUL8	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	powers22b	0	CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents	705	743	705-743	705	false	Powers, Sam and Xing, Eliot and Kolve, Eric and Mottaghi, Roozbeh and Gupta, Abhinav	given family Sam Powers given family Eliot Xing given family Eric Kolve given family Roozbeh Mottaghi given family Abhinav Gupta	2022-11-28		Proceedings of The 1st Conference on Lifelong Learning Agents	199	inproceedings	date-parts 2022 11 28	https://proceedings.mlr.press/v199/powers22b/powers22b.pdf