Pinned Repositories
brooklyn-bikes
Term project
Computational_neuroscience
Everything comp neuro
gpurunner
intro-to-rl
Code exercises from Reinforcement Learning by Sutton and Barto
jraph
A Graph Neural Network Library in Jax
madrona
waymax
A JAX-based simulator for autonomous driving research.
gpudrive
GPU-acceleration of Nocturne via Madrona
nocturne_lab
A data-driven, fast driving simulator for multi-agent coordination under partial observability.
nocturne
A data-driven, fast driving simulator for multi-agent coordination under partial observability.
daphnecor's Repositories
daphnecor/waymax
A JAX-based simulator for autonomous driving research.
daphnecor/intro-to-rl
Code exercises from Reinforcement Learning by Sutton and Barto
daphnecor/brooklyn-bikes
Term project
daphnecor/Computational_neuroscience
Everything comp neuro
daphnecor/gpurunner
daphnecor/jraph
A Graph Neural Network Library in Jax
daphnecor/madrona
daphnecor/madrona_rl_envs
daphnecor/prisoners-dilemma
daphnecor/NeuroAnalysis
Assignments for the Neuro-analysis course 2021
daphnecor/nocturne
A data-driven, fast driving simulator for multi-agent coordination under partial observability.
daphnecor/open_spiel
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
daphnecor/Python_for_DataScience
This contains all the links to colab notebooks used in the Python for Data Science Bootcamp by Turing Students Rotterdam.
daphnecor/robot-league
daphnecor/TME
This code package is for the Tensor-Maximum-Entropy (TME) method. This method generates random surrogate data that preserves a specified set of first and second order marginal moments of a data tensor, which makes it well equipped to test for the null hypothesis that a structure in data is an epiphenomenon of these specified set of primary features of the data tensor. The random surrogate data are sampled from a maximum entropy distribution. This distribution unlike traditional maximum entropy method have constraints on the marginal first and second moments of the tensor mode.
daphnecor/videos