87 research outputs found
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
Many real-world decision problems are characterized by multiple conflicting
objectives which must be balanced based on their relative importance. In the
dynamic weights setting the relative importance changes over time and
specialized algorithms that deal with such change, such as a tabular
Reinforcement Learning (RL) algorithm by Natarajan and Tadepalli (2005), are
required. However, this earlier work is not feasible for RL settings that
necessitate the use of function approximators. We generalize across weight
changes and high-dimensional inputs by proposing a multi-objective Q-network
whose outputs are conditioned on the relative importance of objectives and we
introduce Diverse Experience Replay (DER) to counter the inherent
non-stationarity of the Dynamic Weights setting. We perform an extensive
experimental evaluation and compare our methods to adapted algorithms from Deep
Multi-Task/Multi-Objective Reinforcement Learning and show that our proposed
network in combination with DER dominates these adapted algorithms across
weight change scenarios and problem domains
dsmcFoam+: An OpenFOAM based direct simulation Monte Carlo solver
dsmcFoam+ is a direct simulation Monte Carlo (DSMC) solver for rarefied gas dynamics, implemented within the OpenFOAM software framework, and parallelised with MPI. It is open-source and released under the GNU General Public License in a publicly available software repository that includes detailed documentation and tutorial DSMC gas flow cases. This release of the code includes many features not found in standard dsmcFoam, such as molecular vibrational and electronic energy modes, chemical reactions, and subsonic pressure boundary conditions. Since dsmcFoam+ is designed entirely within OpenFOAMâs C++ object-oriented framework, it benefits from a number of key features: the code emphasises extensibility and flexibility so it is aimed first and foremost as a research tool for DSMC, allowing new models and test cases to be developed and tested rapidly. All DSMC cases are as straightforward as setting up any standard OpenFOAM case, as dsmcFoam+ relies upon the standard OpenFOAM dictionary based directory structure. This ensures that useful pre- and post-processing capabilities provided by OpenFOAM remain available even though the fully Lagrangian nature of a DSMC simulation is not typical of most OpenFOAM applications. We show that dsmcFoam+ compares well to other well-known DSMC codes and to analytical solutions in terms of benchmark results
Benchmark numerical simulations of rarefied non-reacting gas flows using an open-source DSMC code
Validation and verification represent an important element in the development of a computational code. The aim is establish both confidence in the algorithm and its suitability for the intended purpose. In this paper, a direct simulation Monte Carlo solver, called dsmcFoam, is carefully investigated for its ability to solve low and high speed non-reacting gas flows in simple and complex geometries. The test cases are: flow over sharp and truncated flat plates, the Mars Pathfinder probe, a micro-channel with heated internal steps, and a simple micro-channel. For all the cases investigated, dsmcFoam demonstrates very good agreement with experimental and numerical data available in the literature
Randbemerkungen zu Werner Sombarts "Die Juden und das Wirtschaftsleben"
von M. Steckelmache
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