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Exploring applications of deep reinforcement learning for real-world autonomous driving systems
(2019-01)
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements
such as Deepmind’s AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye’s
path ...
Vulnerable road user detection: state-of-the-art and open challenges
(2019)
Correctly identifying vulnerable road users (VRUs), e.g. cyclists and pedestrians, remains one of the most challenging environment perception tasks for autonomous vehicles (AVs). This work surveys the current state-of-the-art ...
Curriculum Learning for Tightly Coupled Multiagent Systems
(2019)
In this paper,we leverage curriculum learning(CL) to improve the performance of multiagent systems(MAS) that are trained
with the cooperative coevolution of artificial neural networks. We design curricula to progressively ...
Equilibria in Multi-Objective Games: a Utility-Based Perspective
(Adaptive and Learning Agents Workshop, 2019)
In multi-objective multi-agent systems (MOMAS), agents explicitly
consider the possible tradeoffs between conflicting objective functions.
We argue that compromises between competing objectives
in MOMAS should be analysed ...