Deep Q-Network for Stochastic Process Environments
This work addresses reinforcement learning challenges in stochastic environments for applications like gaming and finance, but it is incremental as it evaluates existing methods without introducing new paradigms.
The paper tackled the problem of applying reinforcement learning to stochastic process environments with missing information, using Flappy Bird and a stock trading environment as case studies, and identified the most suitable Deep Q-learning network variant for such environments.
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.