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 …

Developing a general algorithm that learns to solve tasks across a wide range of
applications has been a fundamental challenge in artificial intelligence. Although current …

Intelligent agents need to generalize from past experience to achieve goals in complex
environments. World models facilitate such generalization and allow learning behaviors …

Model-free deep reinforcement learning (RL) algorithms have been successfully applied to a
range of challenging sequential decision making and control tasks. However, these methods …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …

Reinforcement learning promises to solve complex sequential-decision problems
autonomously by specifying a high-level reward function only. However, reinforcement …

In this work we aim to solve a large collection of tasks using a single reinforcement learning
agent with a single set of parameters. A key challenge is to handle the increased amount of …

Abstract Model-free deep reinforcement learning (RL) algorithms have been demonstrated
on a range of challenging decision making and control tasks. However, these methods …

We consider the fully decentralized multi-agent reinforcement learning (MARL) problem,
where the agents are connected via a time-varying and possibly sparse communication …