Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities

no code implementations • 22 Jun 2023 • Xudong Shen, Hannah Brown, Jiashu Tao, Martin Strobel, Yao Tong, Akshay Narayan, Harold Soh, Finale Doshi-Velez

There is increasing attention being given to how to regulate AI systems.

Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection

no code implementations • 8 Aug 2020 • Xinyi Xu, Tiancheng Huang, Pengfei Wei, Akshay Narayan, Tze-Yun Leong

This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristic-based subgoal selection, experience replay (Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning (Bengio et al. 2009; Zaremba and Sutskever 2014).

Decision Making Hierarchical Reinforcement Learning +4

Park: An Open Platform for Learning-Augmented Computer Systems

1 code implementation • NeurIPS 2019 • Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, Ravichandra Addanki, Mehrdad Khani Shirkoohi, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Kraska, Dr.Mohammad Alizadeh

We present Park, a platform for researchers to experiment with Reinforcement Learning (RL) for computer systems.

reinforcement-learning Reinforcement Learning (RL)

Restructuring Endpoint Congestion Control

1 code implementation • SIGCOMM '18 2018 • Akshay Narayan, Frank Cangialosi, Deepti Raghavan, Prateesh Goyal Srinivas Narayana, Radhika Mittal, Mohammad Alizadeh, Hari Balakrishnan

Each datapath—such as the Linux kernel TCP, UDP-based QUIC, or kernel-bypass transports like mTCP-on-DPDK—summarizes information about packet round-trip times, receptions, losses, and ECN via a well-defined interface to algorithms running in the off-datapath Congestion Control Plane (CCP).