In online reinforcement learning (RL), efficient exploration remains particularly challenging
in high-dimensional environments with sparse rewards. In low-dimensional environments …

A fascinating aspect of nature lies in its ability to produce a large and diverse collection of
organisms that are all high-performing in their niche. By contrast, most AI algorithms focus …

Exploration is essential for solving complex Reinforcement Learning (RL) tasks. Maximum
State-Visitation Entropy (MSVE) formulates the exploration problem as a well-defined policy …

Learning high-quality $ Q $-value functions plays a key role in the success of many modern
off-policy deep reinforcement learning (RL) algorithms. Previous works primarily focus on …

We propose a novel reinforcement learning algorithm, QD-RL, that incorporates the
strengths of off-policy RL algorithms into Quality Diversity (QD) approaches. Quality-Diversity …

Safety is critical to broadening the application of reinforcement learning (RL). Often, we train
RL agents in a controlled environment, such as a laboratory, before deploying them in the …

Mutual information-based reinforcement learning (RL) has been proposed as a promising
framework for retrieving complex skills autonomously without a task-oriented reward function …

Direct policy optimization in reinforcement learning is usually solved with policy-gradient
algorithms, which optimize policy parameters via stochastic gradient ascent. This paper …

Safety is critical to broadening the application of reinforcement learning (RL). Often, RL
agents are trained in a controlled environment, such as a laboratory, before being deployed …

Policy-gradient algorithms are effective reinforcement learning methods for solving control
problems with continuous state and action spaces. To compute near-optimal policies, it is …