Spiking Networks for Improved Cognitive Abilities of Edge Computing Devices

This concept paper highlights a recently opened opportunity for large scale analytical algorithms to be trained directly on edge devices. Such approach is a response to the arising need of processing data generated by natural person (a human being), also known as personal data. Spiking Neural networks are the core method behind it: suitable for a low latency energy-constrained hardware, enabling local training or re-training, while not taking advantage of scalability available in the Cloud.

Macro action selection with deep reinforcement learning in StarCraft

StarCraft (SC) is one of the most popular and successful Real Time Strategy (RTS) games. In recent years, SC is also considered as a testbed for AI research, due to its enormous state space, hidden information, multi-agent collaboration and so on. Thanks to the annual AIIDE and CIG competitions, a growing number of bots are proposed and being continuously improved. However, a big gap still remains between the top bot and the professional human players. One vital reason is that current bots mainly rely on predefined rules to perform macro actions. These rules are not scalable and efficient enough to cope with the large but partially observed macro state space in SC. In this paper, we propose a DRL based framework to do macro action selection. Our framework combines the reinforcement learning approach Ape-X DQN with Long-Short-Term-Memory (LSTM) to improve the macro action selection in bot. We evaluate our bot, named as LastOrder, on the AIIDE 2017 StarCraft AI competition bots set. Our bot achieves overall 83% win-rate, outperforming 26 bots in total 28 entrants.