2,261 research outputs found
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Developing a safe and efficient collision avoidance policy for multiple
robots is challenging in the decentralized scenarios where each robot generate
its paths without observing other robots' states and intents. While other
distributed multi-robot collision avoidance systems exist, they often require
extracting agent-level features to plan a local collision-free action, which
can be computationally prohibitive and not robust. More importantly, in
practice the performance of these methods are much lower than their centralized
counterparts.
We present a decentralized sensor-level collision avoidance policy for
multi-robot systems, which directly maps raw sensor measurements to an agent's
steering commands in terms of movement velocity. As a first step toward
reducing the performance gap between decentralized and centralized methods, we
present a multi-scenario multi-stage training framework to find an optimal
policy which is trained over a large number of robots on rich, complex
environments simultaneously using a policy gradient based reinforcement
learning algorithm. We validate the learned sensor-level collision avoidance
policy in a variety of simulated scenarios with thorough performance
evaluations and show that the final learned policy is able to find time
efficient, collision-free paths for a large-scale robot system. We also
demonstrate that the learned policy can be well generalized to new scenarios
that do not appear in the entire training period, including navigating a
heterogeneous group of robots and a large-scale scenario with 100 robots.
Videos are available at https://sites.google.com/view/drlmac
Complexity Analysis of Balloon Drawing for Rooted Trees
In a balloon drawing of a tree, all the children under the same parent are
placed on the circumference of the circle centered at their parent, and the
radius of the circle centered at each node along any path from the root
reflects the number of descendants associated with the node. Among various
styles of tree drawings reported in the literature, the balloon drawing enjoys
a desirable feature of displaying tree structures in a rather balanced fashion.
For each internal node in a balloon drawing, the ray from the node to each of
its children divides the wedge accommodating the subtree rooted at the child
into two sub-wedges. Depending on whether the two sub-wedge angles are required
to be identical or not, a balloon drawing can further be divided into two
types: even sub-wedge and uneven sub-wedge types. In the most general case, for
any internal node in the tree there are two dimensions of freedom that affect
the quality of a balloon drawing: (1) altering the order in which the children
of the node appear in the drawing, and (2) for the subtree rooted at each child
of the node, flipping the two sub-wedges of the subtree. In this paper, we give
a comprehensive complexity analysis for optimizing balloon drawings of rooted
trees with respect to angular resolution, aspect ratio and standard deviation
of angles under various drawing cases depending on whether the tree is of even
or uneven sub-wedge type and whether (1) and (2) above are allowed. It turns
out that some are NP-complete while others can be solved in polynomial time. We
also derive approximation algorithms for those that are intractable in general
Cuts and Partitions in Graphs/Trees with Applications
Both the maximum agreement forest problem and the multicut on trees problem are NP-hard, thus cannot be solved efficiently if P /=NP. The maximum agreement forest problem was motivated in the study of evolution trees in bioinformatics, in which we are given two leaf-labeled trees and are asked to find a maximum forest that is a subgraph of both trees. The multicuton trees problem has applications in networks, in which we are given a forest and a set of pairs of termianls and are asked to find a cut that separates all pairs of terminals.
We develop combinatorial and algorithmic techniques that lead to improved parameterized algorithms, approximation algorithms, and kernelization algorithms for these problems. For the maximum agreement forest problem, we proceed from the bottommost level of trees and extend solutions to whole trees. With this technique, we show that the maxi- mum agreement forest problem is fixed-parameterized tractable in general trees, resolving an open problem in this area. We also provide the first constant ratio approximation algorithm for the problem in general trees. For the multicut on trees problem, we take a new look at the problem through the eyes of vertex cover problem. This connection allows us to develop an kernelization algorithm for the problem, which gives an upper bound of O(k3) on the kernel size, significantly improving the previous best upper bound O(k6). We further exploit this connection to give a parameterized algorithm for the problem that runs in time O∗ (1.62k), thus improving the previous best algorithm of running time O∗ (2k). In the protein complex prediction problem, which comes directly from the study of bioinformatics, we are given a protein-protein interaction network, and are asked to find dense regions in this graph. We formulate this problem as a graph clustering problem and develop an algorithm to refine the results for identifying protein complexes. We test our algorithm on yeast protein- protein interaction networks, and we show that our algorithm is able to identify complexes more accurately than other existing algorithms
ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint
Large-scale online recommender system spreads all over the Internet being in
charge of two basic tasks: Click-Through Rate (CTR) and Post-Click Conversion
Rate (CVR) estimations. However, traditional CVR estimators suffer from
well-known Sample Selection Bias and Data Sparsity issues. Entire space models
were proposed to address the two issues via tracing the decision-making path of
"exposure_click_purchase". Further, some researchers observed that there are
purchase-related behaviors between click and purchase, which can better draw
the user's decision-making intention and improve the recommendation
performance. Thus, the decision-making path has been extended to
"exposure_click_in-shop action_purchase" and can be modeled with conditional
probability approach. Nevertheless, we observe that the chain rule of
conditional probability does not always hold. We report Probability Space
Confusion (PSC) issue and give a derivation of difference between ground-truth
and estimation mathematically. We propose a novel Entire Space Multi-Task Model
for Post-Click Conversion Rate via Parameter Constraint (ESMC) and two
alternatives: Entire Space Multi-Task Model with Siamese Network (ESMS) and
Entire Space Multi-Task Model in Global Domain (ESMG) to address the PSC issue.
Specifically, we handle "exposure_click_in-shop action" and "in-shop
action_purchase" separately in the light of characteristics of in-shop action.
The first path is still treated with conditional probability while the second
one is treated with parameter constraint strategy. Experiments on both offline
and online environments in a large-scale recommendation system illustrate the
superiority of our proposed methods over state-of-the-art models. The
real-world datasets will be released
Dephasing of ultracold cesium -Rydberg Electromagnetically Induced Transparency
We study Rydberg electromagnetically induced transparency (EIT) of a cascade
three-level atom involving 80 state in a strong interaction regime
employing a cesium ultracold cloud. In our experiment, a strong coupling laser
couples 6 to 80 transition, while a weak probe, driving
6 to 6 transition, probes the coupling induced EIT signal. At
the two-photon resonance, we observe that the EIT transmission decreases slowly
with time, which is a signature of interaction induced metastability. The
dephasing rate is extracted with optical depth OD =
. We find that the optical depth linearly increases with time
at onset for a fixed probe incident photon number before
saturation. The dephasing rate shows a nonlinear dependence on .
The dephasing mechanism is mainly attributed to the strong dipole-dipole
interactions, which leads to state transfer from to other Rydberg
states. We demonstrate that the typical transfer time obtained
by the state selective field ionization technique is comparable with the decay
time of EIT transmission . The presented experiment
provides a useful tool for investigating the strong nonlinear optical effects
and metastable state in Rydberg many-body systems.Comment: 7 pages, 5 figure
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