add some basic glossary terms

docs/glossary.rst

@@ -3,6 +3,38 @@ Glossary
 
 .. glossary::
 
+ HPO
+ Hyperparameter Optimization. The process of finding the best hyperparameters for a given
+ machine learning model.
+
+ AutoRL
+ Automated Reinforcement Learning. The process of automating the process of applying
+ reinforcement learning to real-world problems.
+
+ AutoML
+ Automated Machine Learning. The process of automating the process of applying machine
+ learning to real-world problems.
+
+ Random Search
+ A hyperparameter optimization algorithm that randomly samples hyperparameters from a
+ predefined search space.
+
  BO
  Bayesian Optimization. A Black-Box optimization algorithm weighing exploration & exploitation
  to find the minimum of its objective.
+
+ Multi-fidelity optimization
+ The process of optimizing a function using multiple levels of fidelity. This can be done by
+ using a surrogate model to approximate the function at different levels of fidelity.
+
+ PPO
+ Proximal Policy Optimization. A policy gradient method that uses a clipped surrogate objective
+ to improve the stability of the learning process.
+
+ DQN
+ Deep Q-Network. A deep reinforcement learning algorithm that uses a neural network to approximate
+ the Q-function.
+
+ SAC 
+ Soft Actor-Critic. An off-policy reinforcement learning algorithm that uses the maximum entropy
+ principle to improve the exploration of the environment.