34,303 research outputs found
Predictions for squeezed back-to-back correlations of and in high-energy heavy-ion collisions by event-by-event hydrodynamics
We calculate the squeezed back-to-back correlation (BBC) functions of and for heavy-ion collisions at RHIC and LHC energies, using
()-dimensional hydrodynamics with fluctuating initial conditions. The BBC
functions averaged over event-by-event calculations for many events for the
hydrodynamic sources are smoothed as a function of the particle momentum. For
heavy-ion collisions of Au+Au at GeV, the BBC functions are
larger than those for collisions of Pb+Pb at TeV. The BBC
of may possibly be observed in peripheral collisions at the RHIC and
LHC energies. It is large for the smaller sources of Cu+Cu collisions at
GeV.Comment: 16 pages, 11 figure
Danger-aware Adaptive Composition of DRL Agents for Self-navigation
Self-navigation, referred as the capability of automatically reaching the
goal while avoiding collisions with obstacles, is a fundamental skill required
for mobile robots. Recently, deep reinforcement learning (DRL) has shown great
potential in the development of robot navigation algorithms. However, it is
still difficult to train the robot to learn goal-reaching and
obstacle-avoidance skills simultaneously. On the other hand, although many
DRL-based obstacle-avoidance algorithms are proposed, few of them are reused
for more complex navigation tasks. In this paper, a novel danger-aware adaptive
composition (DAAC) framework is proposed to combine two individually
DRL-trained agents, obstacle-avoidance and goal-reaching, to construct a
navigation agent without any redesigning and retraining. The key to this
adaptive composition approach is that the value function outputted by the
obstacle-avoidance agent serves as an indicator for evaluating the risk level
of the current situation, which in turn determines the contribution of these
two agents for the next move. Simulation and real-world testing results show
that the composed Navigation network can control the robot to accomplish
difficult navigation tasks, e.g., reaching a series of successive goals in an
unknown and complex environment safely and quickly.Comment: 7 pages, 9 figure
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