The emergence of powerful artificial intelligence (AI) is defining new research directions in
neuroscience. To date, this research has focused largely on deep neural networks trained …

Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing
their relative performance on a large suite of tasks. Most published results on deep RL …

The reinforcement learning (RL) research area is very active, with an important number of
new contributions, especially considering the emergent field of deep RL (DRL). However, a …

In reinforcement learning (RL), it is easier to solve a task if given a good representation.
While deep RL should automatically acquire such good representations, prior work often …

We introduce a new unsupervised pre-training method for reinforcement learning called
APT, which stands for Active Pre-Training. APT learns behaviors and representations by …

We introduce a new unsupervised pretraining objective for reinforcement learning. During
the unsupervised reward-free pretraining phase, the agent maximizes mutual information …

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range
of complex yet specific control tasks. Yet training generalist agents that can quickly adapt to …

Data efficiency is a key challenge for deep reinforcement learning. We address this problem
by using unlabeled data to pretrain an encoder which is then finetuned on a small amount of …

Learning generalizable policies that can adapt to unseen environments remains challenging
in visual Reinforcement Learning (RL). Existing approaches try to acquire a robust …

Acquiring abilities in the absence of a task-oriented reward function is at the frontier of
reinforcement learning research. This problem has been studied through the lens of …