Learning from finite data sets is useful for many real-world applications, where data collection may be difficult and costly.
Batch reinforcement learning is the study of algorithms that can learn from a finite batch of data, without directly interacting with the environment,
however, it suffers from the so called "extrapolation error".
The recent study achieved great progress in off-policy reinforcement learning without exploration where the algorithm can learn from arbitrary,
fixed batch data in the continuous control domain.
Learning from finite data sets is useful for many real-world applications, where data collection may be difficult and costly. Batch reinforcement learning is the study of algorithms that can learn from a finite batch of data, without directly interacting with the environment, however, it suffers from the so called "extrapolation error". The recent study achieved great progress in off-policy reinforcement learning without exploration where the algorithm can learn from arbitrary, fixed batch data in the continuous control domain.
The goal of this project is to study state-of-the-art batch reinforcement learning algorithms and apply these algorithms to quadrotor control.
Potentially, we want to combine it with an event camera.
**Required skills:** Python/C++, reinforcement learning, and deep learning skills.
The goal of this project is to study state-of-the-art batch reinforcement learning algorithms and apply these algorithms to quadrotor control. Potentially, we want to combine it with an event camera. **Required skills:** Python/C++, reinforcement learning, and deep learning skills.