2 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
On the Complexity of Learning to Cooperate with Populations of Socially Rational Agents
Robert Loftin, Saptarashmi Bandyopadhyay, Mustafa Mert Çelikok
Artificially intelligent agents deployed in the real-world will require the ability to reliably \textit{cooperate} with humans (as well as other, heterogeneous AI agents). To provi…
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX
Alexander Rutherford, Benjamin Ellis, Matteo Gallici +18
Benchmarks are crucial in the development of machine learning algorithms, with available environments significantly influencing reinforcement learning (RL) research. Traditionally,…
Targets in Reinforcement Learning to solve Stackelberg Security Games
Saptarashmi Bandyopadhyay, Chenqi Zhu, Philip Daniel +3
Reinforcement Learning (RL) algorithms have been successfully applied to real world situations like illegal smuggling, poaching, deforestation, climate change, airport security, et…
Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach
Rajeev Bhatt Ambati, Saptarashmi Bandyopadhyay, Prasenjit Mitra
Traditional sequence-to-sequence (seq2seq) models and other variations of the attention-mechanism such as hierarchical attention have been applied to the text summarization problem…