Predictions for squeezed back-to-back correlations of ϕϕ\phi\phi and K+K−K^+K^- in high-energy heavy-ion collisions by event-by-event hydrodynamics

We calculate the squeezed back-to-back correlation (BBC) functions of $\phi \phi$ and $K^+K^-$ for heavy-ion collisions at RHIC and LHC energies, using ($2+1$)-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 $\sqrt{s_{NN}}=200$ GeV, the BBC functions are larger than those for collisions of Pb+Pb at $\sqrt{s_{NN}}=2.76$ TeV. The BBC of $\phi\phi$ 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 $\sqrt{s_{NN}}=200$ 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