epignatelli
Currently PhD candidate in RL at UCL. Previously ML Lead at @BuroHappoldEngineering and RA at @ImperialCollegeLondon
University College London (UCL)London
Pinned Repositories
BHoM
The Buildings and Habitats Core object Model repo
cardiax
A python implementation of the fenton karma model using fourth order accuracy central finite difference method, euler update scheme, and jax
discovering-reinforcement-learning-algorithms
A Jax/Stax implementation of the general meta learning paper: Oh, J., Hessel, M., Czarnecki, W.M., Xu, Z., van Hasselt, H.P., Singh, S. and Silver, D., 2020. Discovering reinforcement learning algorithms. Advances in Neural Information Processing Systems, 33.
helx
Interoperating between (Deep) Reiforcement Learning libraries
human-level-control-through-deep-reinforcement-learning
A jax/stax implementation of: Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G. and Petersen, S., 2015. Human-level control through deep reinforcement learning. nature, 518(7540), pp.529-533.
navix
Accelerated minigrid environments with JAX
reinforcement-learning-an-introduction
A python implementation of the concepts in the book "Reinforcement Learning: An Introduction" by R.S. Sutton and A. G. Barto.
scalable-recognition-with-a-vocabulary-tree
A python implementation of the paper "Scalable Recognition with a Vocabulary Tree, D. Nister, H. Stewenius, 2006"
bsuite
bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent
jax
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
epignatelli's Repositories
epignatelli/cardiax
A python implementation of the fenton karma model using fourth order accuracy central finite difference method, euler update scheme, and jax
epignatelli/OpenRGB
epignatelli/aaai-template
latex template for various conferences, as well as wise-man's overleaf (overleaf is terrible!)
epignatelli/boo
The Boo Programming Language.
epignatelli/bsuite
bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent
epignatelli/bump-n-tag
A Github Action to automatically bump and tag master, on merge, with the latest SemVer formatted version. Works on any platform.
epignatelli/cardiax_manuscript
epignatelli/curated-rl-bibtex
A mantained collection of curated bibtex of the most relevant reinforcement learning publications
epignatelli/deep_bisim4control
Learning Invariant Representations for Reinforcement Learning without Reconstruction
epignatelli/epignatelli
epignatelli/Font-Awesome
The iconic SVG, font, and CSS toolkit
epignatelli/gitignore
A collection of useful .gitignore templates
epignatelli/GraphINVENT
Graph neural networks for molecular design.
epignatelli/gridworld-x
epignatelli/gyax
GPU-parallelizable gym environments based on a jax backend
epignatelli/gym-minigrid
Minimalistic gridworld package for OpenAI Gym
epignatelli/gym3
Vectorized interface for reinforcement learning environments
epignatelli/optax
Optax is a gradient processing and optimization library for JAX.
epignatelli/prioritized-experience-replay
epignatelli/publication
epignatelli/pyage2
"Age of Empires II" Learning Environment
epignatelli/python-producer-consumer
epignatelli/pytorch-a2c-ppo-acktr-gail
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
epignatelli/setup
epignatelli/so-simple-theme
A simple Jekyll theme for words and pictures.
epignatelli/spr
Code for "Data-Efficient Reinforcement Learning with Self-Predictive Representations"
epignatelli/synthetic-returns-for-long-term-credit-assignment
epignatelli/the-paper-series
The paper series is a collection of unofficial implementations of reknown Deep Reinforcement Algorithms.
epignatelli/theapa.bst-fixed
epignatelli/tracking-nonstationarity-via-online-importance-sampling