Improvements to ODD Feature Page

- Cleaned up text
- Renamed file
- Updated ML-Agents-Overview to point to new file
- Updated figure to showcase the new “On Demand Decisions” checkbox text

docs/ML-Agents-Overview.md

 must react to events or games where agents can take actions of variable 
 duration. Switching between decision taking at every step and 
 on-demand-decision is one button click away. You can learn more about the 
-on-demand-decision feature [here](Feature-On-Demand-Decision.md).
+on-demand-decision feature [here](Feature-On-Demand-Decisions.md).
 
 * **Memory-enhanced Agents** - In some scenarios, agents must learn to 
 remember the past in order to take the 

docs/Feature-On-Demand-Decisions.md

+# On Demand Decision Making
+
+## Description
+On demand decision making allows agents to request decisions from their 
+brains only when needed instead of receiving decisions at a fixed 
+frequency. This is useful when the agents commit to an action for a 
+variable number of steps or when the agents cannot make decisions 
+at the same time. This typically the case for turn based games, games 
+where agents must react to events or games where agents can take 
+actions of variable duration.
+
+## How to use
+
+To enable or disable on demand decision making, use the checkbox called
+`On Demand Decisions` in the Agent Inspector.
+
+<p align="center">
+    <img src="images/ml-agents-ODD.png" 
+        alt="On Demand Decision" 
+        width="500" border="10" />
+</p>
+
+ * If `On Demand Decisions` is not checked, the Agent will request a new 
+ decision every `Decision Frequency` steps and 
+ perform an action every step. In the example above, 
+ `CollectObservations()` will be called every 5 steps and 
+ `AgentAction()` will be called at every step. This means that the 
+ Agent will reuse the decision the Brain has given it. 
+
+ * If `On Demand Decisions` is checked, the Agent controls when to receive
+ decisions and actions. To do so, the Agent may leverage one or two methods:
+   * `RequestDecision()` Signals that the Agent is requesting a decision.
+   This causes the Agent to collect its observations and ask the Brain for a 
+   decision at the next step of the simulation. Note that when an Agent 
+   requests a decision, it also request an action. 
+   This is to ensure that all decisions lead to an action during training.
+   * `RequestAction()` Signals that the Agent is requesting an action. The
+   action provided to the Agent in this case is the same action that was
+   provided the last time it requested a decision. 

docs/Feature-On-Demand-Decision.md

-# On Demand Decision Making
-
-## Description
-On demand decision making allows agents to request decisions from their 
-brains only when needed instead of requesting decisions at a fixed 
-frequency. This is useful when the agents commit to an action for a 
-variable number of steps or when the agents cannot make decisions 
-at the same time. This typically the case for turn based games, games 
-where agents must react to events or games where agents can take 
-actions of variable duration.
-
-## How to use
-
-In the agent inspector, there is a checkbox called 
-`On Demand Decision`
-
-![Brain Inspector](images/ml-agents-ODD.png)
-
- * If `On Demand Decision` is not checked, all the agents will 
- request a new decision every `Decision Frequency` steps and 
- perform an action every step. In the example above, 
- `CollectObservations()` will be called every 5 steps and 
- `AgentAct()` will be called at every step. This means that the 
- agent will reuse the decision the brain has given it. 
-
- * If `On Demand Decision` is checked, you are in charge of telling 
- the agent when to request a decision and when to request an action. 
- To do so, call the following methods on your agent component.
-   * `RequestDecision()` Call this method to signal the agent that it 
-   must collect its observations and ask the brain for a decision at 
-   the next step of the simulation. Note that when an agent requests 
-   a decision, it will also request an action automatically 
-   (This is to ensure that all decisions lead to an action during training)
-   * `RequestAction()` Call this method to signal the agent that 
-   it must reuse its previous action at the next step of the 
-   simulation. The Agent will not ask the brain for a new decision, 
-   it will just call `AgentAct()` with the same action.