Offline reinforcement learning (RL) Algorithms are often designed with environments such
as MuJoCo in mind, in which the planning horizon is extremely long and no noise exists. We …

Offline reinforcement learning algorithms still lack trust in practice due to the risk that the
learned policy performs worse than the original policy that generated the dataset or behaves …

Recently developed offline reinforcement learning algorithms have made it possible to learn
policies directly from pre-collected datasets, giving rise to a new dilemma for practitioners …

This paper presents the first algorithm for model-based offline quantum reinforcement
learning and demonstrates its functionality on the cart-pole benchmark. The model and the …

Offline reinforcement learning algorithms are still not fully trusted by practitioners due to the
risk that the learned policy performs worse than the original policy that generated the dataset …

This thesis proposes three novel algorithms for offline reinforcement learning, which allow
for training policies from pre-collected datasets without direct environment interaction. The …