Anil A. Bharath

Classifying Options for Deep Reinforcement Learning

2016 · arXiv (Cornell University)

In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the policy network, and a supervisory network …

Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control

2019 · arXiv (Cornell University)

Deep networks have enabled reinforcement learning to scale to more complex and challenging domains, but these methods typically require large quantities of training data. An alternative is to use sample-efficient episodic control methods: neuro-inspired algorithms …

Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks

2022 · arXiv (Cornell University)

Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice. Recent machine-learning approaches have demonstrated their ability to accelerate spatio-temporal predictions, although, with only moderate accuracy …