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Found 6 papers, 2 papers with code
Value Refinement Network (VRN)
Combining the benefits of planning and learning values, we propose the Value Refinement Network (VRN), an architecture that locally refines a plan in a (simpler) state space abstraction, represented by a pre-computed value function, with respect to the full agent state.
Hierarchies of Planning and Reinforcement Learning for Robot Navigation
In simulated robotic navigation tasks, VI-RL results in consistent strong improvement over vanilla RL, is on par with vanilla hierarchal RL on single layouts but more broadly applicable to multiple layouts, and is on par with trainable HL path planning baselines except for a parking task with difficult non-holonomic dynamics where it shows marked improvements.
Reward (Mis)design for Autonomous Driving
This article considers the problem of diagnosing certain common errors in reward design.
On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective
We implement and benchmark parallel I/O methods for the fully-manycore driven particle-in-cell code PIConGPU.
Performance Computational Physics D.4.8; B.4.3; I.6.6
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics
Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent.
Visualizing the Radiation of the Kelvin-Helmholtz Instability
Emerging new technologies in plasma simulations allow tracking billions of particles while computing their radiative spectra.
Plasma Physics Computational Physics 85-08
