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An application of Reinforcement Learning to control dynamic systems

(Eine Anwendung des Reinforcement Learning zur Regelung dynamischer Systeme)

This bachelor thesis implements a way to achieve a reliable and stable control of a dynamic system through approaches of reinforcement learning. For a neuronal network, the "Touch Withdrawal Circuit" of the worm C. Elegans is examined in great detail and the structures are transformed into a simulator. As a simulation environment, the inverted pendulum is being used with one degree of freedom (1 DOF). To simulate the neural network and gurantee reliable control for the inverted pendulum, a simulator is being developed and implemented using the programming languarge Python. Using the well known Leaky Integrate and Fire model, simulation of internal neural dynamics and processing information within the network is made possible. Furthermore, Pparameters of the network are found using reinforcement learning algorithms and applied to the environment CartPole v0 from OpenAI Gym. The result of this work shows, that it is possible to implement a functional simulator for biological neural networks and to link it with methods of reinforcement learning. After computing multiple simulations, suitable Parameters for the network, which ensure stable control of the inverse pendulum, are found. An application to other simulation environments or with similar neural networks is also possible due to the structure of the simulator.

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This Repo includes the source Code of the TW Circuit Simulator and some other Informations about my Thesis. Also some simulation renderings are included.