Solutions and figures for problems from Reinforcement Learning: An Introduction Sutton&Barto

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

The goal of this project is to build an RL-based algorithm that can help cab drivers maximize their profits by improving their decision-making process on the field. Taking long-term profit as the goal, a method is proposed based on reinforcement learning to optimize taxi driving strategies for profit maximization. This optimization problem is fo…

Code and data for the paper "A Macroeconomic Approach to Optimal Unemployment Insurance: Applications"

Computing optimal MDP policy using Value Iteration Algorithm and Linear Programming

Code and data for the paper "Optimal Public Expenditure with Inefficient Unemployment"

ImpRator (Inverse Method for Policy with Reward AbstracT behaviOR) is a prototype implementation to compute parameter valuations in parametric Markov decision processes such that optimal policies remain optimal.