Reinforcement Learning Algorithms with Python

Policy gradient methods

The algorithms that have been learned and developed so far are value-based, which, at their core, learn a value function, V(s), or action-value function, Q(s, a). A value function is a function that defines the total reward that can be accumulated from a given state or state-action pair. An action can then be selected, based on the estimated action (or state) values.

Therefore, a greedy policy can be defined as follows:

Value-based methods, when combined with deep neural networks, can learn very sophisticated policies in order to control agents that operate in high-dimensionality spaces. Despite these great qualities, ...