onesamblack's Stars
hawemily/transformers-for-stock-price-prediction
xbpeng/DeepMimic
Motion imitation with deep reinforcement learning.
openai/baselines
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
seungeunrho/minimalRL
Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
zplizzi/pytorch-ppo
Simple, readable, yet full-featured implementation of PPO in Pytorch
bentrevett/pytorch-rl
Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]
patelayush/Dijkstra-s-Algorithm-Using-min-Binary-Heap
IntroductionConsider a data communication network that must route data packets (email, MP3 files, or videofiles, for example). Such a network consists of routers connected by physical cables or links. Arouter can act as a source, a destination, or a forwarder of data packets. We can model a networkas a graph with each router corresponding to a vertex and the link or physical connection betweentwo routers corresponding to apair of directed edgesbetween the vertices.A network that follows the OSPF (Open Shortest Path First) protocol routes packets usingDijkstra’s shortest path algorithm. The criteria used to compute the weight corresponding to alink can include the time taken for data transmission, reliability of the link, transmission cost, andavailable bandwidth. Typically each router has a complete representation of the network graphand associated information available to it.For the purposes of this project, each link has associated with it the transmission time takenfor data to get from the vertex at one end to the vertex at the other end. You will compute thebest path using the criterion of minimizing the total time taken for data to reach the destination.The shortest time path minimizes the sum of the transmission times of the links along the path.The network topology can change dynamically based on the state of the links and the routers.For example, a link may go down when the corresponding cable is cut, and a vertex may go downwhen the corresponding router crashes. In addition to these transient changes, changes to a networkoccur when a link is added or removed.
stepjam/RLBench
A large-scale benchmark and learning environment.
google-deepmind/lab
A customisable 3D platform for agent-based AI research
ARISE-Initiative/robosuite
robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
google-deepmind/rlax
google-deepmind/optax
Optax is a gradient processing and optimization library for JAX.
danielmiessler/SecLists
SecLists is the security tester's companion. It's a collection of multiple types of lists used during security assessments, collected in one place. List types include usernames, passwords, URLs, sensitive data patterns, fuzzing payloads, web shells, and many more.
pytorch/serve
Serve, optimize and scale PyTorch models in production
google/jax
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
google/flax
Flax is a neural network library for JAX that is designed for flexibility.
ctallec/world-models
Reimplementation of World-Models (Ha and Schmidhuber 2018) in pytorch
rail-berkeley/rlkit
Collection of reinforcement learning algorithms
rail-berkeley/softlearning
Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
Zymrael/awesome-neural-ode
A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
DiffEqML/torchdyn
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
google/automl
Google Brain AutoML
rtqichen/torchdiffeq
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
fastapi/fastapi
FastAPI framework, high performance, easy to learn, fast to code, ready for production
facebookresearch/hydra
Hydra is a framework for elegantly configuring complex applications
y788zhan/joint_seg_flow
skorch-dev/skorch
A scikit-learn compatible neural network library that wraps PyTorch
bndr/pipreqs
pipreqs - Generate pip requirements.txt file based on imports of any project. Looking for maintainers to move this project forward.
kynan/nbstripout
strip output from Jupyter and IPython notebooks
HarisIqbal88/PlotNeuralNet
Latex code for making neural networks diagrams