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V-D4RL

Introduced in Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations2022

About this Dataset

V-D4RL provides pixel-based analogues of the popular D4RL benchmarking tasks, derived from the dm_control suite, along with natural extensions of two state-of-the-art online pixel-based continuous control algorithms, DrQ-v2 and DreamerV2, to the offline setting.

Source: Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations

Dataset Variants

V-D4RL

Papers1

Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations

This paper introduces a suite of benchmarking tasks and shows that simple modifications to two popular vision-based online reinforcement learning algorithms, DreamerV2 and DrQ-v2, suffice to outperform existing offline RL methods and establish competitive baselines for continuous control in the visual domain.

Tasks

EDIT

Reinforcement Learning (RL)

Similar Datasets

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Statistics

Papers

1

Tasks

66

Introduced

2022

License

MIT

Modalities

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Languages

English