Unsupervised Reinforcement Learning with Contrastive Intrinsic Control

Neural Information Processing Systems (NeurIPS 2022)

Michael Laskin (1)    Hao Liu (1)    Xue Bin Peng (1)    Denis Yarats (2,3)    Aravind Rajeswaran (3)    Pieter Abbeel (1,4)

(1) University of California, Berkeley    (2) New York University    (3) MetaAI    (4) Covariant.


We introduce Contrastive Intrinsic Control (CIC), an unsupervised reinforcement learning (RL) algorithm that maximizes the mutual information between statetransitions and latent skill vectors. CIC utilizes contrastive learning between state-transitions and skills vectors to learn behaviour embeddings and maximizes the entropy of these embeddings as an intrinsic reward to encourage behavioural diversity. We evaluate our algorithm on the Unsupervised RL Benchmark (URLB) in the asymptotic state-based setting, which consists of a long reward-free pretraining phase followed by a short adaptation phase to downstream tasks with extrinsic rewards. We find that CIC improves over prior exploration algorithms in terms of adaptation efficiency to downstream tasks on state-based URLB.

Paper: [PDF]       Webpage: [Link]       Code: [GitHub]       Preprint: [arXiv]


	author = {Laskin, Michael and Liu, Hao and Peng, Xue Bin and Yarats, Denis and Rajeswaran, Aravind and Abbeel, Pieter},
	booktitle = {Advances in Neural Information Processing Systems},
	editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
	pages = {34478--34491},
	publisher = {Curran Associates, Inc.},
	title = {Unsupervised Reinforcement Learning with Contrastive Intrinsic Control},
	url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/debf482a7dbdc401f9052dbe15702837-Paper-Conference.pdf},
	volume = {35},
	year = {2022}