Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence
(AI) and represents a step toward building autonomous systems with a higher-level …

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step
towards building autonomous systems with a higher level understanding of the visual world …

In this work, we present a scalable reinforcement learning method for training multi-task
policies from large offline datasets that can leverage both human demonstrations and …

Many real-world applications require artificial agents to compete and coordinate with other
agents in complex environments. As a stepping stone to this goal, the domain of StarCraft …

Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning
(RL). We present a novel technique called Hindsight Experience Replay which allows …

We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear
preferences, with the goal of enabling few-shot adaptation to new tasks. In MORL, the aim is …

This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent
deep reinforcement learning (DRL) in unbalanced distribution systems. This method is novel …

Solving complex, temporally-extended tasks is a long-standing problem in reinforcement
learning (RL). We hypothesize that one critical element of solving such problems is the …

Discrete-action algorithms have been central to numerous recent successes of deep
reinforcement learning. However, applying these algorithms to high-dimensional action …

Model-free reinforcement learning (RL) is a powerful, general tool for learning complex
behaviors. However, its sample efficiency is often impractically large for solving challenging …