Reinforcement learning is applied to solve actual complex tasks from high-dimensional, sensory inputs. The last decade has developed a long list of reinforcement learning algorithms. Recent progress benefits from deep learning for raw sensory signal representation. One question naturally arises: how well do they perform concerning different robotic manipulation tasks? Benchmarks use objective performance metrics to offer a scientific way to compare algorithms. In this paper, we present RMBench, the first benchmark for robotic manipulations, which have high-dimensional continuous action and state spaces. We implement and evaluate reinforcement learning algorithms that directly use observed pixels as inputs. We report their average performance and learning curves to show their performance and stability of training. Our study concludes that none of the studied algorithms can handle all tasks well, soft Actor-Critic outperforms most algorithms in average reward and stability, and an algorithm combined with data augmentation may facilitate learning policies. Our code is publicly available at https://github.com/xiangyanfei212/RMBench-2022, including all benchmark tasks and studied algorithms.

The lacking of analytic solutions of diverse partial differential equations (PDEs) gives birth to series of computational techniques for numerical solutions. In machine learning, numerous latest advances of solver designs are accomplished in developing neural operators, a kind of mesh-free approximators of the infinite-dimensional operators that map between different parameterization spaces of equation solutions. Although neural operators exhibit generalization capacities for learning an entire PDE family simultaneously, they become less accurate and explainable while learning long-term behaviours of non-linear PDE families. In this paper, we propose Koopman neural operator (KNO), a new neural operator, to overcome these challenges. With the same objective of learning an infinite-dimensional mapping between Banach spaces that serves as the solution operator of target PDE family, our approach differs from existing models by formulating a non-linear dynamic system of equation solution. By approximating the Koopman operator, an infinite-dimensional linear operator governing all possible observations of the dynamic system, to act on the flow mapping of dynamic system, we can equivalently learn the solution of an entire non-linear PDE family by solving simple linear prediction problems. In zero-shot prediction and long-term prediction experiments on representative PDEs (e.g., the Navier-Stokes equation), KNO exhibits notable advantages in breaking the tradeoff between accuracy and efficiency (e.g., model size) while previous state-of-the-art models are limited. These results suggest that more efficient PDE solvers can be developed by the joint efforts from physics and machine learning.

Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants. However, current ground-level NO2 concentration data are lack of either high-resolution coverage or full coverage national wide, due to the poor quality of source data and the computing power of the models. To our knowledge, this study is the first to estimate the ground-level NO2 concentration in China with national coverage as well as relatively high spatiotemporal resolution (0.25 degree; daily intervals) over the newest past 6 years (2013-2018). We advanced a Random Forest model integrated K-means (RF-K) for the estimates with multi-source parameters. Besides meteorological parameters, satellite retrievals parameters, we also, for the first time, introduce socio-economic parameters to assess the impact by human activities. The results show that: (1) the RF-K model we developed shows better prediction performance than other models, with cross-validation R2 = 0.64 (MAPE = 34.78%). (2) The annual average concentration of NO2 in China showed a weak increasing trend . While in the economic zones such as Beijing-Tianjin-Hebei region, Yangtze River Delta, and Pearl River Delta, the NO2 concentration there even decreased or remained unchanged, especially in spring. Our dataset has verified that pollutant controlling targets have been achieved in these areas. With mapping daily nationwide ground-level NO2 concentrations, this study provides timely data with high quality for air quality management for China. We provide a universal model framework to quickly generate a timely national atmospheric pollutants concentration map with a high spatial-temporal resolution, based on improved machine learning methods.