daphnecor

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_rl_envs

prisoners-dilemma

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.

daphnecor/waymax

A JAX-based simulator for autonomous driving research.

daphnecor/brooklyn-bikes

Term project

daphnecor/Computational_neuroscience

Everything comp neuro

daphnecor/gpurunner

daphnecor/intro-to-rl

Code exercises from Reinforcement Learning by Sutton and Barto

daphnecor/jraph

A Graph Neural Network Library in Jax

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.