Search Results for author:
Found 8 papers, 3 papers with code
Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning
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.
GLIDE-RL: Grounded Language Instruction through DEmonstration in RL
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.
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning
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.
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning
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
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
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.
Maximum Reward Formulation In Reinforcement Learning
Reinforcement learning (RL) algorithms typically deal with maximizing the expected cumulative return (discounted or undiscounted, finite or infinite horizon).
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
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.
