In the last few years, deep multi-agent reinforcement learning (RL) has become a highly
active area of research. A particularly challenging class of problems in this area is partially …

Abstract Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the
development of challenging environments that test the limits of current methods. While …

Deep reinforcement learning has gathered much attention recently. Impressive results were
achieved in activities as diverse as autonomous driving, game playing, molecular …

Humans learn best from feedback—we are encouraged to take actions that lead to positive
results while deterred by decisions with negative consequences. This reinforcement process …

Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence
(AI) by endowing autonomous systems with high levels of understanding of the real world …

Deep reinforcement learning has shown remarkable success in the past few years. Highly
complex sequential decision making problems from game playing and robotics have been …

Deep reinforcement learning has shown remarkable success in the past few years. Highly
complex sequential decision making problems have been solved in tasks such as game …

We consider the problem of spatial path planning. In contrast to the classical solutions which
optimize a new plan from scratch and assume access to the full map with ground truth …

Solving robotic navigation tasks via reinforcement learning (RL) is challenging due to their
sparse reward and long decision horizon nature. However, in many navigation tasks, high …

After artificial intelligent (AI) scientists have conquered Go game, StarCraft has been the next
biggest challenge. A highly intelligent AI system that is able to beat human professional …