EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine

There has been significant progress in developing reinforcement learning (RL) training systems. Past works such as IMPALA, Apex, Seed RL, Sample Factory, and others aim to improve the system's overall throughput. In this paper, we try to address a common bottleneck in the RL training system, i.e., parallel environment execution, which is often the slowest part of the whole system but receives little attention. With a curated design for paralleling RL environments, we have improved the RL environment simulation speed across different hardware setups, ranging from a laptop, and a modest workstation, to a high-end machine like NVIDIA DGX-A100. On a high-end machine, EnvPool achieves 1 million frames per second for the environment execution on Atari environments and 3 million frames per second on MuJoCo environments. When running on a laptop, the speed of EnvPool is 2.8 times of the Python subprocess. Moreover, great compatibility with existing RL training libraries has been demonstrated in the open-sourced community, including CleanRL, rl_games, DeepMind Acme, etc. Finally, EnvPool allows researchers to iterate their ideas at a much faster pace and has the great potential to become the de facto RL environment execution engine. Example runs show that it takes only 5 minutes to train Atari Pong and MuJoCo Ant, both on a laptop. EnvPool has already been open-sourced at https://github.com/sail-sg/envpool.

The Application of Active Query K-Means in Text Classification

Active learning is a state-of-art machine learning approach to deal with an abundance of unlabeled data. In the field of Natural Language Processing, typically it is costly and time-consuming to have all the data annotated. This inefficiency inspires out our application of active learning in text classification. Traditional unsupervised k-means clustering is first modified into a semi-supervised version in this research. Then, a novel attempt is applied to further extend the algorithm into active learning scenario with Penalized Min-Max-selection, so as to make limited queries that yield more stable initial centroids. This method utilizes both the interactive query results from users and the underlying distance representation. After tested on a Chinese news dataset, it shows a consistent increase in accuracy while lowering the cost in training.