Datasets and Benchmarks: Dataset and Benchmark Poster Session 3

URLB: Unsupervised Reinforcement Learning Benchmark

Misha Laskin · Denis Yarats · Hao Liu · Kimin Lee · Albert Zhan · Kevin Lu · Catherine Cang · Lerrel Pinto · Pieter Abbeel

[ Abstract ] [ Chat [ Paper ]


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.

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