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Found 7 papers, 3 papers with code
Robust Reinforcement Learning Objectives for Sequential Recommender Systems
Attention-based sequential recommendation methods have shown promise in accurately capturing users' evolving interests from their past interactions.
Generalizing Successor Features to continuous domains for Multi-task Learning
In this paper, we consider the problem of continuous control for various robot manipulation tasks with an explicit representation that promotes skill reuse while learning multiple tasks, related through the reward function.
Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop
This report presents the debates, posters, and discussions of the Sim2Real workshop held in conjunction with the 2020 edition of the "Robotics: Science and System" conference.
Intervention Design for Effective Sim2Real Transfer
The goal of this work is to address the recent success of domain randomization and data augmentation for the sim2real setting.
Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models
Unlike the majority of existing methods that assume optimal demonstrations and incorporate the demonstration data as hard constraints on policy optimization, we instead incorporate demonstration data as advice in the form of a reward shaping potential trained as a generative model of states and actions.
Learning Domain Randomization Distributions for Training Robust Locomotion Policies
We explore the use of gradient-based search methods to learn a domain randomization with the following properties: 1) The trained policy should be successful in environments sampled from the domain randomization distribution 2) The domain randomization distribution should be wide enough so that the experience similar to the target robot system is observed during training, while addressing the practicality of training finite capacity models.
Joint 3D Proposal Generation and Object Detection from View Aggregation
We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios.
