Hands-On Machine Learning for Algorithmic Trading

The deep Q-learning algorithm and extensions

Deep Q learning estimates the value of the available actions for a given state using a deep neural network. It was introduced by Deep Mind's Playing Atari with Deep Reinforcement Learning (2013: https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf), where RL agents learned to play games solely from pixel input.

The deep Q-learning algorithm approximates the action-value function, q, by learning a set of weights, θ, of a multi-layered Deep Q Network (DQN) that maps states to actions so that .

The algorithm applies gradient descent to a loss function defined as the squared difference between the DQN's estimate ...