Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent learning (MAL) scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. The primary goal of this extended abstract is to provide a broad overview of current multiagent deep reinforcement learning (MDRL) literature, hopefully motivating the reader to review our 47- page JAAMAS survey article . Additionally, we complement the overview with a broader analysis: (i) We revisit previous key components, originally presented in MAL and RL, and highlight how they have been adapted to multiagent deep reinforcement learning settings. (ii) We provide general guidelines to new practitioners in the area: describing lessons learned from MDRL works, pointing to recent benchmarks, and outlining open avenues of research. (iii) We take a more critical tone raising practical challenges of MDRL.
Feb 15th 2022
Read this research paper, co-authored by Amii Fellow and Canada CIFAR AI Chair Adam White: Learning Expected Emphatic Traces for Deep RL
Feb 15th 2022
Read this research paper, co-authored by Canada CIFAR AI Chair Kevin Leyton-Brown: The Perils of Learning Before Optimizing
Feb 14th 2022
Read this research paper, co-authored by Amii Fellows and Canada CIFAR AI Chairs Osmar Zaïane,and Lili Mou, Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision
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