Active observing in continuous-time control
The control of continuous-time environments while actively deciding when to take costly
observations in time is a crucial yet unexplored problem, particularly relevant to real-world …
observations in time is a crucial yet unexplored problem, particularly relevant to real-world …
An Idiosyncrasy of Time-discretization in Reinforcement Learning
Many reinforcement learning algorithms are built on an assumption that an agent interacts
with an environment over fixed-duration, discrete time steps. However, physical systems are …
with an environment over fixed-duration, discrete time steps. However, physical systems are …
Mitigating the curse of horizon in Monte-Carlo returns
The standard framework in reinforcement learning (RL) dictates that an agent should use
every observation collected from interactions with the environment when updating its value …
every observation collected from interactions with the environment when updating its value …
Reinforcement Learning Through the Lens of Koopman Operators-The Infinite-Dimensional Framework
F Zanini - 2023 - research.unipd.it
Reinforcement Learning is nowadays one of the most active research areas in Artificial
Intelligence. This is due both to its practical successes, and to the theoretical challenges it …
Intelligence. This is due both to its practical successes, and to the theoretical challenges it …