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Financial Trading as a Game: A Deep Reinforcement Learning Approach · Around

2018

Financial Trading as a Game: A Deep Reinforcement Learning Approach

Huang, Chien Yi

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An automatic program that generates constant profit from the financial market is lucrative for every market practitioner.

Recent advance in deep reinforcement learning provides a framework toward end-to-end training of such trading agent.
In this paper, we propose an Markov Decision Process (MDP) model suitable for the financial trading task and solve it with the state-of-the-art deep recurrent Q-network (DRQN) algorithm.
We propose several modifications to the existing learning algorithm to make it more suitable under the financial trading setting, namely 1.

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