Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning

1 code implementation • ICML 2020 • Sai Krishna Gottipati, Boris Sattarov, Sufeng. Niu, Hao-Ran Wei, Yashaswi Pathak, Shengchao Liu, Simon Blackburn, Karam Thomas, Connor Coley, Jian Tang, Sarath Chandar, Yoshua Bengio

In this work, we propose a novel reinforcement learning (RL) setup for drug discovery that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo compound design system.

Drug Discovery Navigate +3

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)

Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning

no code implementations • 19 Dec 2023 • Rupali Bhati, Sai Krishna Gottipati, Clodéric Mars, Matthew E. Taylor

While there has been significant progress in curriculum learning and continuous learning for training agents to generalize across a wide variety of environments in the context of single-agent reinforcement learning, it is unclear if these algorithms would still be valid in a multi-agent setting.

Multi-agent Reinforcement Learning reinforcement-learning +1

Human-Machine Teaming for UAVs: An Experimentation Platform

no code implementations • 18 Dec 2023 • Laila El Moujtahid, Sai Krishna Gottipati, Clodéric Mars, Matthew E. Taylor

With this platform, we hope to facilitate further research on human-machine teaming in critical systems and defense environments.

Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operations

no code implementations • 21 Jun 2021 • AI Redefined, Sai Krishna Gottipati, Sagar Kurandwad, Clodéric Mars, Gregory Szriftgiser, François Chabot

Involving humans directly for the benefit of AI agents' training is getting traction thanks to several advances in reinforcement learning and human-in-the-loop learning.

reinforcement-learning Reinforcement Learning (RL)

Maximum Reward Formulation In Reinforcement Learning

1 code implementation • 8 Oct 2020 • Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall, Sahir, Raviteja Chunduru, Ahmed Touati, Sriram Ganapathi Subramanian, Matthew E. Taylor, Sarath Chandar

Reinforcement learning (RL) algorithms typically deal with maximizing the expected cumulative return (discounted or undiscounted, finite or infinite horizon).

Drug Discovery reinforcement-learning +1

Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

1 code implementation • 26 Apr 2020 • Sai Krishna Gottipati, Boris Sattarov, Sufeng. Niu, Yashaswi Pathak, Hao-Ran Wei, Shengchao Liu, Karam M. J. Thomas, Simon Blackburn, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio

Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep generative models.

Drug Discovery Navigate +2