Reinforcement Learning for Controlled Fractional Dynamics

Student: Kaftanov Ilia

Supervisor: Maria Veretennikova

Educational Programme: Statistical Modelling and Actuarial Science (Master)

Year of Graduation: 2020

In this work we study the possibility of using neural networks for controlling fractional dynamics problems. We designed an environment where the agent is controlling the directions of the jumps and a reinforcement learning algorithm DDPG is used to approximate the optimal policy. We consider the possibility of using a neural network to approximate the state value function obtained by dynamic programming for fractional dynamics modeling controlled anomalous diffusion.

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