URLB: Unsupervised Reinforcement Learning Benchmark
This provides a unified benchmark for researchers to evaluate unsupervised RL algorithms, addressing a key bottleneck in the field, though it is incremental as it builds on existing frameworks.
The paper tackles the challenge of comparing and developing unsupervised reinforcement learning (RL) methods by introducing the Unsupervised Reinforcement Learning Benchmark (URLB), which includes twelve continuous control tasks and shows that current baselines make progress but cannot fully solve it.
Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks. Yet 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.