We present a novel observation about the behavior of offline reinforcement learning (RL)
algorithms: on many benchmark datasets, offline RL can produce well-performing and safe …

Offline reinforcement learning is important in domains such as medicine, economics, and e-
commerce where online experimentation is costly, dangerous or unethical, and where the …

Identifying the trade-offs between model-based and model-free methods is a central
question in reinforcement learning. Value-based methods offer substantial computational …

As robots venture into real-world applications, there is an increasing need for them to
effectively learn from experience and adapt to unseen situations. This thesis addresses a …

Healthcare applications pose significant challenges to existing reinforcement learning (RL)
methods due to implementation risks, low data availability, short treatment episodes, sparse …

We present a novel observation about the behavior of offline reinforcement learning (RL)
algorithms: on many benchmark datasets, offline RL can produce well-performing and safe …