According to cognitive psychology and related disciplines, the development of complex
problem-solving behaviour in biological agents depends on hierarchical cognitive …

This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …

Building autonomous machines that can explore open-ended environments, discover
possible interactions and build repertoires of skills is a general objective of artificial …

We propose a novel policy update that combines regularized policy optimization with model
learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero's state-of-the …

Simulation is a crucial tool for accelerating the development of autonomous vehicles.
Making simulation realistic requires models of the human road users who interact with such …

Abstract Model-based reinforcement learning approaches add explicit domain knowledge to
agents in hopes of improving the sample-efficiency in comparison to model-free agents …

While reinforcement learning (RL) methods that learn an internal model of the environment
have the potential to be more sample efficient than their model-free counterparts, learning to …

In robotic domains, learning and planning are complicated by continuous state spaces,
continuous action spaces, and long task horizons. In this work, we address these challenges …

Model-based reinforcement learning is a compelling framework for data-efficient learning of
agents that interact with the world. This family of algorithms has many subcomponents that …

Diffusion-based generative methods have proven effective in modeling trajectories with
offline datasets. However, they often face computational challenges and can falter in …