Deep Active Localization. - Sai Krishna Gottipati; Keehong Seo; Dhaivat Bhatt; Vincent Mai; Krishna Murthy 001; Liam Paull (IEEE Robotics Autom. Lett., 2019)

Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. - Sai Krishna Gottipati; Boris Sattarov; Sufeng Niu; Yashaswi Pathak; Haoran Wei; Shengchao Liu; Simon Blackburn; Karam M. J. Thomas; Connor W. Coley; Jian Tang 005; Sarath Chandar; Yoshua Bengio (ICML, 2020)

Maximum Reward Formulation In Reinforcement Learning. - Sai Krishna Gottipati; Yashaswi Pathak; Rohan Nuttall; Sahir; Raviteja Chunduru; Ahmed Touati; Sriram Ganapathi Subramanian; Matthew E. Taylor; Sarath Chandar (CoRR, 2020)

MapLite: Autonomous Intersection Navigation Without a Detailed Prior Map. - Teddy Ort; Krishna Murthy 001; Rohan Banerjee; Sai Krishna Gottipati; Dhaivat Bhatt; Igor Gilitschenski; Liam Paull; Daniela Rus (IEEE Robotics Autom. Lett., 2020)

Towered Actor Critic For Handling Multiple Action Types In Reinforcement Learning For Drug Discovery. - Sai Krishna Gottipati; Yashaswi Pathak; Boris Sattarov; Sahir; Rohan Nuttall; Mohammad Amini; Matthew E. Taylor; Sarath Chandar (AAAI, 2021)

Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operations. - A. I. Redefined; Sai Krishna Gottipati; Sagar Kurandwad; Clodéric Mars; Gregory Szriftgiser; François Chabot (CoRR, 2021)

Hiking up that HILL with Cogment-Verse: Train & Operate Multi-agent Systems Learning from Humans. - Sai Krishna Gottipati; Luong-Ha Nguyen; Clodéric Mars; Matthew E. Taylor (AAMAS, 2023)

Human-Machine Teaming for UAVs: An Experimentation Platform. - Laila El Moujtahid; Sai Krishna Gottipati; Clodéric Mars; Matthew E. Taylor (CoRR, 2023)

Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning. - Rupali Bhati; Sai Krishna Gottipati; Clodéric Mars; Matthew E. Taylor (CoRR, 2023)

GLIDE-RL: Grounded Language Instruction through DEmonstration in RL. - Chaitanya Kharyal; Sai Krishna Gottipati; Tanmay Kumar Sinha; Srijita Das 001; Matthew E. Taylor (AAMAS, 2024)

Generative Active Learning for the Search of Small-molecule Protein Binders. - Maksym Korablyov; Cheng-Hao Liu; Moksh Jain; Almer M. van der Sloot; Eric Jolicoeur; Edward Ruediger; Andrei Cristian Nica; Emmanuel Bengio; Kostiantyn Lapchevskyi; Daniel St-Cyr; Doris Alexandra Schuetz; Victor Ion Butoi; et al. (CoRR, 2024)

Do as you teach: a multi-teacher approach to self-play in deep reinforcement learning. - Chaitanya Kharyal; Sai Krishna Gottipati; Tanmay Kumar Sinha; Fatemeh Abdollahi; Srijita Das 001; Matthew E. Taylor (Neural Comput. Appl., 2025)

A Systematic Approach to Design Real-World Human-in-the-Loop Deep Reinforcement Learning: Salient Features, Challenges and Trade-offs. - Jalal Arabneydi; Md. Saiful Islam 007; Srijita Das 001; Sai Krishna Gottipati; William Duguay; Clodéric Mars; Matthew E. Taylor; Matthew Guzdial; Antoine Fagette; Younes Zerouali (CoRR, 2025)

Human-AI collaboration in real-world complex environment with reinforcement learning. - Md. Saiful Islam 007; Srijita Das 001; Sai Krishna Gottipati; William Duguay; Clodéric Mars; Jalal Arabneydi; Antoine Fagette; Matthew Guzdial; Matthew E. Taylor (Neural Comput. Appl., 2025)