no code implementations • 3 Jan 2024 • Chaitanya Kharyal, Sai Krishna Gottipati, Tanmay Kumar Sinha, Srijita Das, Matthew E. Taylor
However, training efficient Reinforcement Learning (RL) agents grounded in natural language has been a long-standing challenge due to the complexity and ambiguity of the language and sparsity of the rewards, among other factors.
no code implementations • 23 Dec 2023 • Md Saiful Islam, Srijita Das, Sai Krishna Gottipati, William Duguay, Clodéric Mars, Jalal Arabneydi, Antoine Fagette, Matthew Guzdial, Matthew-E-Taylor
In this work, we show that learning from humans is effective and that human-AI collaboration outperforms human-controlled and fully autonomous AI agents in a complex simulation environment.
no code implementations • 13 Oct 2022 • Michael Guevarra, Srijita Das, Christabel Wayllace, Carrie Demmans Epp, Matthew E. Taylor, Alan Tay
We propose an AI-based pilot trainer to help students learn how to fly aircraft.
no code implementations • 14 Apr 2022 • Sahir, Ercüment İlhan, Srijita Das, Matthew E. Taylor
Reinforcement learning (RL) has shown great success in solving many challenging tasks via use of deep neural networks.
no code implementations • 10 Jun 2020 • Srijita Das, Sriraam Natarajan, Kaushik Roy, Ronald Parr, Kristian Kersting
We consider the problem of Approximate Dynamic Programming in relational domains.