Reward Conditioned Neural Movement Primitives for Population-Based Variational Policy Optimization. Abstract: This paper aims to study the reward-based policy exploration problem in a supervised learning approach and enable robots to form complex movement trajectories in challenging reward settings and search spaces.
Abstract— The aim of this paper is to study the reward based policy exploration problem in a supervised learning approach and enable robots to form complex ...
The experience of the robot is used to train a novel Neural Processes-based deep network that samples from its latent space and generates the required ...
This paper aims to study the reward-based policy exploration problem in a supervised learning approach and enable robots to form complex movement ...
Bibliographic details on Reward Conditioned Neural Movement Primitives for Population Based Variational Policy Optimization.
RC-NMP. Reward Conditioned Neural Movement Primitives for Population Based Variational Policy Optimization. This directory contains code for the experiments ...
Jan 3, 2021 · Reward Conditioned Neural Movement Primitives for Population Based Variational Policy Optimization · Comments.
Reward Conditioned Neural Movement Primitives for Population-Based Variational Policy Optimization. MT Akbulut, U Bozdogan, A Tekden, E Ugur.
This paper proposes a novel neural network model capable of discovering high-level skill representations from unlabeled demonstration data.
May 29, 2024 · Reward Conditioned Neural Movement Primitives for Population Based Variational Policy Optimization. CoRR abs/2011.04282 (2020). [+] ...