D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning

Understand

Offline reinforcement learning algorithms hold the promise of enabling data-driven RL methods that do not require costly or dangerous real-world exploration and benefit from large pre-collected datasets.

• This in turn can facilitate real-world applications, as well as a more standardized approach to RL research.
• Furthermore, offline RL methods can provide effective initializations for online finetuning to overcome challenges with exploration.
• However, evaluating progress on offline RL algorithms requires effective and challenging benchmarks that capture properties of real-world tasks, provide a range of task difficulties, and cover a range of challenges both in terms of the parameters of the domain (e.g., length of the horizon, sparsity of rewards) and the parameters of the data (e.g., narrow demonstration data or broad exploratory data).