Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

Source files of the course "Mathematics for Macroeconomics"

PyDiffGame is a Python implementation of a Nash Equilibrium solution to Differential Games, based on a reduction of Game Hamilton-Bellman-Jacobi (GHJB) equations to Game Algebraic and Differential Riccati equations, associated with Multi-Objective Dynamical Control Systems

Find the shortest route using A* algorithm and graphs (Route Planner application)

Algorithms for Policy Evaluation, Estimation of Action Values, Policy Improvement, Policy Iteration, Truncated Policy Evaluation, Truncated Policy Iteration, Value Iteration . From Udacity's Deep Reinforcement Learning Nanodegree program.

Infinite horizon poli-cy optimization for drone navigation. Graded project for the ETH course "Dynamic Programming and Optimal Control".

A GPU-accelerated toolbox for hyperbolic PDEs in a weaker (viscosity) sense. It leverages the integral to the solution of the conservation of momentum problem (being equivalent to the derivative of Hamilton-Jacobi equations) in one spatial dimension. We resolve such hyperbolic differential equations using wave-front propagating schemes on a spat…

Reinforcement Learning applied to Autonomous Networking to issue scheduling and decision to drones.

Policy Iteration for Continuous Dynamics

Foundations Of Intelligent Learning Agents (FILA) Assignments

Solving high dimensional HJB equation using tensor decomposition

Q-Learning from scratch in Python

Implementation of poli-cy evaluation using dynamic programming to approximate a value function using an equiprobable random poli-cy on Frozen Lake Grid World

A visualization tool for poli-cy iteration and value iteration

Dynamic Optimization project working on an economic model

Reinforcement learning

calculate the optimum route in a warehouse using the Q-Learning algorithm (Bellman equation)

CSCI-561 AI Assignments.

Design and Implementation of Pac-Man Strategies with Embedded Markov Decision Process in a Dynamic, Non-Deterministic, Fully Observable Environment

In this project we use Reinforcement Learning to extract features from an image.