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 …

Generative flow networks (GFlowNets) are a family of algorithms for training a sequential
sampler of discrete objects under an unnormalized target density and have been …

Convex Reinforcement Learning (RL) is a recently introduced framework that generalizes
the standard RL objective to any convex (or concave) function of the state distribution …

Generative flow networks (GFlowNets) are amortized variational inference algorithms that
treat sampling from a distribution over compositional objects as a sequential decision …

Recent years have witnessed great potential in applying Deep Reinforcement Learning
(DRL) in various challenging applications, such as autonomous driving, nuclear fusion …

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

We address the challenge of exploration in reinforcement learning (RL) when the agent
operates in an unknown environment with sparse or no rewards. In this work, we study the …

A promising technique for exploration is to maximize the entropy of visited state distribution,
ie, state entropy, by encouraging uniform coverage of visited state space. While it has been …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

In the absence of assigned tasks, a learning agent typically seeks to explore its environment
efficiently. However, the pursuit of exploration will bring more safety risks. An under-explored …