Popular GitHub Repositories for Simulated Annealing

Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

AI constraint solver in Java to optimize the vehicle routing problem, employee rostering, task assignment, maintenance scheduling, conference scheduling and other planning problems.

A framework for comprehensive diagnosis and optimization of agents using simulated, realistic synthetic interactions

Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training"

An agent benchmark with tasks in a simulated software company.

Implementation of Apple's Learning from Simulated and Unsupervised Images through Adversarial Training

A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.

A Multi-Task Dataset for Simulated Humanoid Control

Deep Reinforcement Learning for mobile robot navigation in IR-SIM simulation. Using DRL (SAC, TD3, PPO, DDPG) neural networks, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

Deep Reinforcement Learning for mobile robot navigation in ROS2 Gazebo simulator. Using DRL (SAC, TD3) neural networks, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

Steps to reproduce training results for the paper Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?

Use Unity to generate synthetic images for deep learning image segmentation in PyTorch and fastai

A software tool that generates simulated radar signals and creates RF datasets for developing and testing machine/deep learning detection algorithms.

Deep Neural Network architecture as a predictive optimal controller for {HVAC+Solar cell + battery} disturbance afflicted system vs classic Model Predictive Control

Python code for Deep Reinforcement Learning with Transfer Learning in a Simulated Environment

A comprehensive framework for reinforcement learning in robotics, which allows users to train their robots in both simulated and real-world environments.

Customisable Unified Physical Simulations (CUPS) for Reinforcement Learning. Experiments run on the ai2thor environment (http://ai2thor.allenai.org/) e.g. using A3C, RainbowDQN and A3C_GA (Gated Attention multi-modal fusion) for Task-Oriented Language Grounding (tasks specified by natural language instructions) e.g. "Pick up the Cup or else"