URLB: Unsupervised Reinforcement Learning Benchmark

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks. Training generalist agents that can quickly adapt to new tasks remains an outstanding challenge. Recent advances in unsupervised RL have shown that pre-training RL agents with self-supervised intrinsic rewards can result in efficient adaptation. However, these algorithms have been hard to compare and develop due to the lack of a unified benchmark. To this end, we introduce the Unsupervised Reinforcement Learning Benchmark (URLB). URLB consists of two phases: reward-free pre-training and downstream task adaptation with extrinsic rewards. Building on the DeepMind Control Suite, we provide twelve continuous control tasks from three domains for evaluation and open-source code for eight leading unsupervised RL methods. We find that the implemented baselines make progress but are not able to solve URLB and propose directions for future research.

Authors

• Misha Laskin
• Denis Yarats
• Hao Liu The Hong Kong University of Science and Technology (Guangzhou) The Hong Kong University of Science and Technology
• Kimin Lee Korea Advanced Institute of Science & Technology
• Albert Zhan
• Kevin Lu
• Catherine Cang
• Lerrel Pinto New York University
• Pieter Abbeel UC Berkeley

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