50,157 research outputs found
A Periodic Systems Toolbox for MATLAB
The recently developed Periodic Systems Toolbox for MATLAB is described. The basic approach to develop this toolbox was to exploit the powerful object manipulation features of MATLAB via flexible andfunctionally rich high level m-functions, while simultaneously enforcing highly efficient and numerically sound computations via the mex-function technology of MATLAB to solve critical numerical problems.The m-functions based user interfaces ensure user-friendliness in operating with the functions of this toolbox via an object oriented approach to handle periodic system descriptions. The mex-functions are based on Fortran implementations of recently developed structure exploiting and structure preserving numerical algorithms for periodic systems which completely avoid forming of lifted representations
Reinforcement Learning With Temporal Logic Rewards
Reinforcement learning (RL) depends critically on the choice of reward
functions used to capture the de- sired behavior and constraints of a robot.
Usually, these are handcrafted by a expert designer and represent heuristics
for relatively simple tasks. Real world applications typically involve more
complex tasks with rich temporal and logical structure. In this paper we take
advantage of the expressive power of temporal logic (TL) to specify complex
rules the robot should follow, and incorporate domain knowledge into learning.
We propose Truncated Linear Temporal Logic (TLTL) as specifications language,
that is arguably well suited for the robotics applications, together with
quantitative semantics, i.e., robustness degree. We propose a RL approach to
learn tasks expressed as TLTL formulae that uses their associated robustness
degree as reward functions, instead of the manually crafted heuristics trying
to capture the same specifications. We show in simulated trials that learning
is faster and policies obtained using the proposed approach outperform the ones
learned using heuristic rewards in terms of the robustness degree, i.e., how
well the tasks are satisfied. Furthermore, we demonstrate the proposed RL
approach in a toast-placing task learned by a Baxter robot
A conjugate gradient minimisation approach to generating holographic traps for ultracold atoms
Direct minimisation of a cost function can in principle provide a versatile
and highly controllable route to computational hologram generation. However, to
date iterative Fourier transform algorithms have been predominantly used. Here
we show that the careful design of cost functions, combined with numerically
efficient conjugate gradient minimisation, establishes a practical method for
the generation of holograms for a wide range of target light distributions.
This results in a guided optimisation process, with a crucial advantage
illustrated by the ability to circumvent optical vortex formation during
hologram calculation. We demonstrate the implementation of the conjugate
gradient method for both discrete and continuous intensity distributions and
discuss its applicability to optical trapping of ultracold atoms.Comment: 11 pages, 4 figure
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