GLIDE-RL: Grounded Language Instruction through DEmonstration in RL

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.

Continual Learning reinforcement-learning +1

Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning

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.

reinforcement-learning Reinforcement Learning (RL)

Augmenting Flight Training with AI to Efficiently Train Pilots

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.

Methodical Advice Collection and Reuse in Deep Reinforcement Learning

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.

Atari Games reinforcement-learning +1

Fitted Q-Learning for Relational Domains

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.

Q-Learning