Solutions and figures for problems from Reinforcement Learning: An Introduction Sutton&Barto
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Updated
Jul 16, 2019 - Python
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…
Implementation of the MDP algorithm for optimal decision-making, focusing on value iteration and policy determination.
Computing optimal MDP policy using Value Iteration Algorithm and Linear Programming
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.
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