Search Results for author:
Found 3 papers, 1 papers with code
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow.
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment
In this paper, we investigate the use of empowerment in the presence of an extrinsic reward signal.
Disentangled Skill Embeddings for Reinforcement Learning
We propose a novel framework for multi-task reinforcement learning (MTRL).
Hierarchical Reinforcement Learning
reinforcement-learning
+2
