Dara: Dynamics-aware reward augmentation in offline reinforcement learning
Offline reinforcement learning algorithms promise to be applicable in settings where a fixed
dataset is available and no new experience can be acquired. However, such formulation is …
dataset is available and no new experience can be acquired. However, such formulation is …
A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning
Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient,
adaptive, and explainable by learning an explicit model of the environment. While the …
adaptive, and explainable by learning an explicit model of the environment. While the …