Pinned Repositories
bbob_numpy
Numpy implementation of the Noiseless Functions of the Black-Box Optimization Benchmarking Suite
cem
Parallelized Cross Entropy Method
cluster_work
Framework for repetitive evaluation of experiments with large sets of parameters
diss_drawio
facmac
LMRS
Source code for ICLR 2020 paper: "Learning to Guide Random Search"
MORE
pybrain
spqrel_tools
Development scripts from the SPQR-eL team
VectorizedMultiAgentSimulator
VMAS is a vectorized framework designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
maxhuettenrauch's Repositories
maxhuettenrauch/bbob_numpy
Numpy implementation of the Noiseless Functions of the Black-Box Optimization Benchmarking Suite
maxhuettenrauch/MORE
maxhuettenrauch/cem
Parallelized Cross Entropy Method
maxhuettenrauch/cluster_work
Framework for repetitive evaluation of experiments with large sets of parameters
maxhuettenrauch/diss_drawio
maxhuettenrauch/facmac
maxhuettenrauch/LMRS
Source code for ICLR 2020 paper: "Learning to Guide Random Search"
maxhuettenrauch/pybrain
maxhuettenrauch/spqrel_tools
Development scripts from the SPQR-eL team
maxhuettenrauch/VectorizedMultiAgentSimulator
VMAS is a vectorized framework designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
maxhuettenrauch/wandb2numpy
Library for easy data export from WandB to NumPy or CSV.