Current research directions in deep reinforcement learning include bridging the simulation-
reality gap, improving sample efficiency of experiences in distributed multi-agent …

Many real-world applications require multi-robot coordination in partially-observable
domains such as package delivery, search, and rescue. One typical way to address partial …

Applying Deep Reinforcement Learning (DRL) to complex tasks in the field of robotics has
proven to be very successful in the recent years. However, most of the publications focus …

Multi-robot systems can benefit from reinforcement learning (RL) algorithms that learn
behaviours in a small number of trials, a property known as sample efficiency. This research …

Multi-agent reinforcement learning (MARL) has been gaining extensive attention from
academia and industries in the past few decades. One of the fundamental problems in …

Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

Deep reinforcement learning has recently seen huge success across multiple areas in the
robotics domain. Owing to the limitations of gathering real-world data, ie, sample inefficiency …

The DARPA subterranean challenge requires teams of robots to traverse difficult and
diverse underground environments. Traversing small gaps is one of the challenging …

Deep multi-agent reinforcement learning (MARL) holds the promise of automating many real-
world cooperative robotic manipulation and transportation tasks. Nevertheless …

The success of reinforcement learning for real world robotics has been, in many cases
limited to instrumented laboratory scenarios, often requiring arduous human effort and …