209,543 research outputs found
Performance Evaluation of Vision-Based Algorithms for MAVs
An important focus of current research in the field of Micro Aerial Vehicles
(MAVs) is to increase the safety of their operation in general unstructured
environments. Especially indoors, where GPS cannot be used for localization,
reliable algorithms for localization and mapping of the environment are
necessary in order to keep an MAV airborne safely. In this paper, we compare
vision-based real-time capable methods for localization and mapping and point
out their strengths and weaknesses. Additionally, we describe algorithms for
state estimation, control and navigation, which use the localization and
mapping results of our vision-based algorithms as input.Comment: Presented at OAGM Workshop, 2015 (arXiv:1505.01065
Benchmarking Deep Reinforcement Learning for Continuous Control
Recently, researchers have made significant progress combining the advances
in deep learning for learning feature representations with reinforcement
learning. Some notable examples include training agents to play Atari games
based on raw pixel data and to acquire advanced manipulation skills using raw
sensory inputs. However, it has been difficult to quantify progress in the
domain of continuous control due to the lack of a commonly adopted benchmark.
In this work, we present a benchmark suite of continuous control tasks,
including classic tasks like cart-pole swing-up, tasks with very high state and
action dimensionality such as 3D humanoid locomotion, tasks with partial
observations, and tasks with hierarchical structure. We report novel findings
based on the systematic evaluation of a range of implemented reinforcement
learning algorithms. Both the benchmark and reference implementations are
released at https://github.com/rllab/rllab in order to facilitate experimental
reproducibility and to encourage adoption by other researchers.Comment: 14 pages, ICML 201
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