Implementation and visualization (some demos) of search and optimization algorithms.
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Updated
Nov 30, 2021 - Python
Implementation and visualization (some demos) of search and optimization algorithms.
Model-based reinforcement learning using CEM, MPC and PETS
Cross Entropy Method (CEM) implemented under Pytorch, supporting batch dimension and receding horizon style optimization.
CE-ABC is a code to simulate the epidemic outbreaks with mechanistic models through a cross-entropy approximate Bayesian framework.
Solving Tetris using Cross-Entropy Method
Simulation experiments for optimizing objective function with Differential Evolution, Evolution Strategies and Cross Entropy Method (2 versions)
Reinforcement Learning Notebooks
Simple implementation and comparison of three reinforcement learning models.
Model-Based RL Multi-Tasking with ReLAx
Train a Cross-Entropy Method in Policy-Based Methods with OpenAI Gtm's MountainCarContinous environment
CEopt is a Matlab routine for non-convex optimization using the Cross-Entropy method and augmented Lagrangian formulation.
Two dimensional optimisation algorithm using the Cross Entropy Method. Data is iteratively fitted to a Beta Distribution in the algorithm.
Tools for using motion primitives like Dynamic Motion Primitives or Differentiable Linear Dynamic Systems in PyTorch.
Workshop code for the talk on Introduction to Reinforcement Learning: https://fosterelli.co/file/talk/introduction-to-reinforcement-learning.pdf
Cross-Entropy method example on OpenAI Gym's MountainCarContinuous environment. Code is from Udacity's "Deep Reinforcement Learning Nanodegree Program"
Open AI Cartpole environment gradient ascent
Automated tuning of hyperparameters using Cross Entropy Method for optimization (CEM).
A Monte Carlo method for importance sampling and optimization.
Fifth assignment for Machine Learning course @USI19/20.
CartPole-CrossEntropyMethod
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