On our path toward fully autonomous systems, i.e., systems that operate in the real world without significant human intervention, reinforcement learning (RL) is a promising framework for learning to solve problems by trial and error. While RL has had …
On our path toward fully autonomous systems, i.e., systems that operate in the real world without significant human intervention, reinforcement learning (RL) is a promising framework for learning to solve problems by trial and error. While RL has had …
On our path toward fully autonomous systems, i.e., systems that operate in the real world without significant human intervention, reinforcement learning (RL) is a promising framework for learning to solve problems by trial and error. While RL has had …
In robot learning we face challenge of data-efficient learning. In this talk, we will make the case for three types of useful models that become handy in robot learning: probabilistic models, hierarchical models, and models that allow us to …
Optimal control has seen many success stories over the past decades. However, when it comes to autonomous systems in open-ended settings, we require methods that allow for automatic learning from data. Reinforcement learning is a principled …
In many practical applications of machine learning, we face the challenge of data-efficient learning, i.e., learning from scarce data. This includes healthcare, climate science, and autonomous robots. There are many approaches toward learning from …
In many high-impact areas of machine learning, we face the challenge of data-efficient learning, i.e., learning from scarce data. This includes healthcare, climate science, and autonomous robots. There are many approaches toward learning from scarce …
Optimal control has seen many success stories over the past decades. However, when it comes to autonomous systems in open-ended settings, we require methods that allow for automatic learning from data. Reinforcement learning is a principled …
On our path toward fully autonomous systems, i.e., systems that operate in the real world without significant human intervention, reinforcement learning (RL) is a promising framework for learning to solve problems by trial and error. While RL has had …
I will be talking about different aspects of uncertainty when doing reinforcement learning, e.g., model uncertainty, how to use it for exploration, some traps, how to use uncertainty for safe exploration etc.