Cooperative Multi-agent Reinforcement Learning (MARL) algorithms with
Ze...
Efficient exploration is critical in cooperative deep Multi-Agent
Reinfo...
One of the key behavioral characteristics used in neuroscience to determ...
Decentralized cooperative multi-agent deep reinforcement learning (MARL)...
In recent years, a growing number of deep model-based reinforcement lear...
Learning to flexibly follow task instructions in dynamic environments po...
The COVID-19 pandemic, like many of the disease outbreaks that have prec...
Meta-learning algorithms aim to learn two components: a model that predi...
We present a method for learning intrinsic reward functions to drive the...
Arguably, intelligent agents ought to be able to discover their own ques...
Neural end-to-end goal-oriented dialog systems showed promise to reduce ...
In a dialog, there can be multiple valid next utterances at any point. T...
Many natural language processing tasks require dealing with Named Entiti...
Interlingua based Machine Translation (MT) aims to encode multiple langu...
Recently there has been a lot of interest in learning common representat...
Transferring knowledge from prior source tasks in solving a new target t...