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 …

Efficient exploration under sparse rewards remains a key challenge in deep reinforcement
learning. Previous exploration methods (eg, RND) have achieved strong results in multiple …

Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …

Quality-Diversity (QD) algorithms, and MAP-Elites (ME) in particular, have proven very useful
for a broad range of applications including enabling real robots to recover quickly from joint …

In this article, we investigate the content transmission in a heavy-crowded multiple access
cellular network, whose data traffic is offloaded through the combination of edge caching …

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

Efficient exploration under sparse rewards remains a key challenge in deep reinforcement
learning. To guide exploration, previous work makes extensive use of intrinsic reward (IR) …

We present COIL (Counterfactual Object Interaction Learning), a novel way of learning skills
of object interactions on entity-centric environments. The goal is to learn primitive behaviors …

Efficient exploration remains a challenging problem in reinforcement learning, especially for
tasks where extrinsic rewards from environments are sparse or even totally disregarded …

Reinforcement learning (RL) has achieved promising results in solving numerous
challenging sequential decision problems. To address the issue of sparse extrinsic rewards …