Investigation and understanding of nonlinear dynamic systems are crucial in various applications and fields of research as describing chaotic systems in physics , comprehend bio-molecular processes for designing new medial drugs or in engineering as for developing new high-performance materials . Simultaneously, such systems’ global dynamics are notoriously hard to represent, although differential equations to describe such systems are well-known in some cases. A standard tool for analyzing dynamic behavior over time are dynamic simulations . However, these techniques often suffer from a very high dimensional data space resulting in enormous effort to create suitable dimensionality reduction methods .

Hence, we investigate a practical and efficient tool to describe and analyze complex dynamic systems, namely symbolic dynamics . We derive symbolic dynamic systems by representing the phase spaces as partitions endowed with the Markov property, called Markov partitions. Such phase space discretizations facilitate analyzing and computing global dynamics. Further, they make dynamic systems accessible to optimal sequential decision-making algorithms. Thus, we extend the work of to bridge a substantial gap in research work by developing algorithms to construct Markov partitions for various dynamic systems automatically. Further, we formalize and implement a framework to build Markov decision processes for dynamic systems based on Markov partitions. We show how to fuse our work with approximate dynamic programming and apply this fused framework in experiments executed in dynamic system environments.

Moreover, our experiments with Markov partitions provide evidence for superior convergence performance of iterative Monte Carlo based policy evaluation algorithms compared to regular grid-like discretizations. However, we also experience mathematically inherent limits while constructing Markov partitions for a broad class of dynamic systems. Additionally, it is not clear how to entirely transport Markov partitions’ topological and measure-theoretic properties beyond the application to some fixed policy evaluation step.

• experimental/notebooks contains jupyter notebooks providing code for applying implemented algorithms, toy examples and performed experiments
  • torus-toy-example-construction.ipynb contains the implementation of the construction work done in chapter 4.1
  • markov-partition-construction-algorithms.py.ipynb contains the algorithmic constructions done in chapter 4.2
  • monte-carlo-experiments.ipynb contains the Monte Carlo estimation algorithms experiment results evaluated in chapter 6.1
  • dynamic-programming-experiments.ipynb contains the DP-algorithm experiment results evaluated in chapter 6.2
• experimental/utils contains wrappers for frequently used code and proposed algorithms
  • dynamic_system.py is a wrapper for defining system dynamics and computing (hyperbolic) fixed points
  • partition.py is an implementation of the proposed Markov partition construction alogrithm in chapter chapter 4.2 given a dynamic system
  • markov_decision_process.py is a parallelized implementation of the proposed Monte Carlo estimation algorithms in chapter 5 given a Markov partition
• deployment/ contains everything required for a docker-compose setup of the whole pipeline

1. Introduction
2. On Symbolic Dynamics for Representations of Dynamic Systems
   1. Shift Spaces
   2. Shifts of Finite Type
   3. From Dynamic Systems to Shifts of Finite Type
3. From Dynamic Systems to Markov Partitions and Symbolic Dynamics
   1. Topological Universality of Toral Endomorphisms
   2. Locally Split Anosov-Smale Hyperbolic Systems
   3. From Differentiable to Combinatorial Systems
   4. On Markov Partitions to Construct Symbolic Systems
4. Algorithmic Construction of Markov Partitions
   1. Implementation of Markov Partitions for a Toy Exmaple
   2. Algorithmic Construction of Markov Partitions
5. From Markov Partitions to Markov Decision Processes
6. Experiments and Applications
   1. Experiments - Monte Carlo Algorithms
   2. Experiments - Dynamic Programming Algorithms
   3. Discussion of Real-World Applications
7. Related Work
   1. Origins in the Theory of Dynamic Systems
   2. Symbolic Dynamics and Markov Partitions
   3. Representation Learning in Dynamic Programming
8. Conclusion and Future Work

1. cd deployment/
2. docker-compose build
3. docker-compose up
4. Open jupyterlab link printed in terminal in a browser of your choice and start exploring the notebooks