Rudimentary Heuristic Brain docs

docs/Learning-Environment-Heuristic-Brains.md

 # Heuristic Brain
 
+The **Heuristic** brain type allows you to hand code an agent's decision making process. A Heuristic brain requires an implementation of the Decision interface to which it delegates the decision making process.
+
+When you set the **Brain Type** property of a Brain to **Heuristic**, you must add a component implementing the Decision interface to the same GameObject as the Brain. 
+
+## Implementing the Decision interface
+
+When creating your Decision class, extend MonoBehaviour (so you can use the class as a Unity component) and extend the Decision interface.
+
+    using UnityEngine;
+    
+    public class HeuristicLogic : MonoBehaviour, Decision 
+    {
+        // ...
+    }
+
+The Decision interface defines two methods, `Decide()` and `MakeMemory()`. 
+
+The `Decide()` method receives an agents current state, consisting of the agent's observations, reward, memory and other aspects of the agent's state, and must return an array containing the action that the agent should take. The format of the returned action array depends on the **Vector Action Space Type**. When using a **Continuous** action space, the action array is just a float array with a length equal to the **Vector Action Space Size** setting. When using a **Discrete** action space, the array contains just a single value. In the discrete action space, the **Space Size** value defines the number of discrete values that your `Decide()` function can return, which don't need to be consecutive integers. 
+
+The `MakeMemory()` function allows you to pass data forward to the next iteration of an agen't decision making process. The array you return from `MakeMemory()` is passed to the `Decide()` function in the next iteration. You can use the memory to allow the agent's decision process to take past actions and observations into account when making the current decision. If your heuristic logic does not require memory, just return an empty array.