Reinforcement Learning (RL) researchers at Froxt develop AI agents that can learn to solve tasks in an unknown environment by interacting with it over time. RL agents can enable significant improvements in a broad range of applications, from personal assistants that naturally interact with people and adapt to their needs, to autonomous robots that can readily adjust to changing environments
At Froxt, our research spans several aspects of RL, including sample efficiency of deep RL algorithms, theoretical aspects of RL algorithms, RL algorithms integrating inputs from multiple sources (e.g., language), RL agents integrating real-world constraints (e.g., fairness, privacy, and security), RL agents for human interaction, multi-agent RL, and self-supervised RL.