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.