Adult semantic memory has been traditionally conceptualized as a relatively static memory
system that consists of knowledge about the world, concepts, and symbols. Considerable …

Artificial intelligence (AI) and extended reality (XR) differ in their origin and primary
objectives. However, their combination is emerging as a powerful tool for addressing …

Abstract Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning
algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent …

Large sequence models (SM) such as GPT series and BERT have displayed outstanding
performance and generalization capabilities in natural language process, vision and …

The availability of challenging benchmarks has played a key role in the recent progress of
machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi …

Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve
complex cooperative tasks. Its success is partly because of parameter sharing among …

Learning to combine control at the level of joint torques with longer-term goal-directed
behavior is a long-standing challenge for physically embodied artificial agents. Intelligent …

High-performing teams learn effective communication strategies to judiciously share
information and reduce the cost of communication overhead. Within multi-agent …

We present Habitat 3.0: a simulation platform for studying collaborative human-robot tasks in
home environments. Habitat 3.0 offers contributions across three dimensions:(1) Accurate …

Offline multi-agent reinforcement learning is challenging due to the coupling effect of both
distribution shift issue common in offline setting and the high dimension issue common in …