Given a short query with keyphrases about a research idea in 2019 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: An unsupervised approach to learn action embeddings from raw pixels to improve sample efficiency of RL algorithms.

Keyphrases: reinforcement learning pixel observations

Dataset description:
Kinetics-600: The Kinetics-600 is a large-scale action recognition dataset which consists of around 480K videos from 600 action categories. The 480K videos are divided into 390K, 30K, 60K for training, validation and test sets, respectively.
MuJoCo: MuJoCo (multi-joint dynamics with contact) is a physics engine used to implement environments to benchmark Reinforcement Learning methods.
MTL-AQA: A new multitask action quality assessment (AQA) dataset, the largest to date, comprising of more than 1600 diving samples; contains detailed annotations for  fine-grained action recognition, commentary generation, and estimating the AQA score. Videos from multiple angles provided wherever available.
2D-3D-S: The 2D-3D-S dataset provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with instance-level semantic and geometric annotations. It covers over 6,000 m2 collected in 6 large-scale indoor areas that originate from 3 different buildings.

Options:- A: Kinetics-600  B: MuJoCo  C: MTL-AQA  D: 2D-3D-S
MuJoCo