Deep Q-Network for Stochastic Process Environments
Kuangheng He
Reinforcement learning is a powerful approach for training an optimal policy
to solve complex problems in a given system. This project aims to demonstrate
the application of reinforcement learning in stochastic process environments
with missing information, using Flappy Bird and a newly developed stock trading
environment as case studies. We evaluate various structures of Deep Q-learning
networks and identify the most suitable variant for the stochastic process
environment. Additionally, we discuss the current challenges and propose
potential improvements for further work in environment-building and
reinforcement learning techniques.