  In stock trading, feature extraction and trading strategy design are the two
important tasks to achieve long-term benefits using machine learning
techniques. Several methods have been proposed to design trading strategy by
acquiring trading signals to maximize the rewards. In the present paper the
theory of deep reinforcement learning is applied for stock trading strategy and
investment decisions to Indian markets. The experiments are performed
systematically with three classical Deep Reinforcement Learning models Deep
Q-Network, Double Deep Q-Network and Dueling Double Deep Q-Network on ten
Indian stock datasets. The performance of the models are evaluated and
comparison is made.
