Search Results for author:
Found 9 papers, 0 papers with code
Controlling the Solo12 Quadruped Robot with Deep Reinforcement Learning
We detail the learning procedure and method for transfer on the real robot.
Learning Synthetic to Real Transfer for Localization and Navigational Tasks
The navigation pipeline is decomposed as a localization module, a planning module and a local navigation module.
Improving the Generalization of Visual Navigation Policies using Invariance Regularization
The results of this method, called invariance regularization, show an improvement in the generalization of policies to environments not seen during training.
DEEP ADVERSARIAL FORWARD MODEL
More precisely, our forward model is trained to produce realistic observations of the future while a discriminator model is trained to distinguish between real images and the model’s prediction of the future.
Contextual memory bandit for pro-active dialog engagement
In this paper, we propose to introduce the paradigm of contextual bandits as framework for pro-active dialog systems.
Non-Markovian Control with Gated End-to-End Memory Policy Networks
In this paper, we explore the use of a recently proposed attention-based model, the Gated End-to-End Memory Network, for sequential control.
Optimal Policies for Observing Time Series and Related Restless Bandit Problems
We discuss computation of that index, give closed-form formulae for it, and compare the performance of the associated index policy with heuristic policies.
When are Kalman-filter restless bandits indexable?
We study the restless bandit associated with an extremely simple scalar Kalman filter model in discrete time.
Transferring Expectations in Model-based Reinforcement Learning
We study how to automatically select and adapt multiple abstractions or representations of the world to support model-based reinforcement learning.
Model-based Reinforcement Learning
reinforcement-learning
+2
