Unsupervised pre-training strategies have proven to be highly effective in natural language
processing and computer vision. Likewise, unsupervised reinforcement learning (RL) holds …

Biological agents demonstrate a remarkable proficiency in calibrating appropriate scales of
planning and evaluation when interacting with their environments. It follows logically that …

Unsupervised reinforcement learning aims at learning a generalist policy in a reward-free
manner for fast adaptation to downstream tasks. Most of the existing methods propose to …

Since the time of Turing, computer scientists have dreamed of building artificial general
intelligence (AGI)-a system that can think, learn and act as humans do. Over recent years …

A key component of model-based reinforcement learning (RL) is a dynamics model that
predicts the outcomes of actions. Errors in this predictive model can degrade the …

Self-supervision has the potential to transform reinforcement learning (RL), paralleling the
breakthroughs it has enabled in other areas of machine learning. While self-supervised …

Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient,
adaptive, and explainable by learning an explicit model of the environment. While the …

In an unfamiliar setting, a model-based reinforcement learning agent can be limited by the
accuracy of its world model. In this work, we present a novel, training-free approach to …

Both entropy-minimizing and entropy-maximizing (curiosity) objectives for unsupervised
reinforcement learning (RL) have been shown to be effective in different environments …

In reinforcement learning, a reward function is used to guide the agent's behavior towards
task-specific objectives. However, such extrinsic rewards often fall short in complex …