diff --git a/docs/Getting-Started-with-Balance-Ball.md b/docs/Getting-Started-with-Balance-Ball.md
index dbffa34a51..02a40a59eb 100644
--- a/docs/Getting-Started-with-Balance-Ball.md
+++ b/docs/Getting-Started-with-Balance-Ball.md
@@ -84,7 +84,7 @@ The Ball3DAgent subclass defines the following methods:
   negative reward for dropping the ball. An Agent is also marked as done when it
   drops the ball so that it will reset with a new ball for the next simulation
   step.
-* agent.Heuristic() - When the `Use Heuristic` checkbox is checked in the Behavior
+* agent.Heuristic() - When the `Behavior Type` is set to `Heuristic Only` in the Behavior
   Parameters of the Agent, the Agent will use the `Heuristic()` method to generate
   the actions of the Agent. As such, the `Heuristic()` method returns an array of
   floats. In the case of the Ball 3D Agent, the `Heuristic()` method converts the
diff --git a/docs/Learning-Environment-Best-Practices.md b/docs/Learning-Environment-Best-Practices.md
index 69b0665581..72ec011056 100644
--- a/docs/Learning-Environment-Best-Practices.md
+++ b/docs/Learning-Environment-Best-Practices.md
@@ -8,8 +8,9 @@
   lessons which progressively increase in difficulty are presented to the agent
   ([learn more here](Training-Curriculum-Learning.md)).
 * When possible, it is often helpful to ensure that you can complete the task by
-  using a heuristic to control the agent. To do so, check the `Use Heuristic`
-  checkbox on the Agent and implement the `Heuristic()` method on the Agent.
+  using a heuristic to control the agent. To do so, set the `Behavior Type`
+  to `Heuristic Only` on the Agent's Behavior Parameters, and implement the
+   `Heuristic()` method on the Agent.
 * It is often helpful to make many copies of the agent, and give them the same
   `Behavior Name`. In this way the learning process can get more feedback
   information from all of these agents, which helps it train faster.
diff --git a/docs/Learning-Environment-Create-New.md b/docs/Learning-Environment-Create-New.md
index 7a1144dc60..1a5830e694 100644
--- a/docs/Learning-Environment-Create-New.md
+++ b/docs/Learning-Environment-Create-New.md
@@ -380,8 +380,8 @@ What this code means is that the heuristic will generate an action corresponding
 to the values of the "Horizontal" and "Vertical" input axis (which correspond to
 the keyboard arrow keys).
 
-In order for the Agent to use the Heuristic, You will need to check the `Use Heuristic`
-checkbox in the `Behavior Parameters` of the RollerAgent.
+In order for the Agent to use the Heuristic, You will need to set the `Behavior Type`
+to `Heuristic Only` in the `Behavior Parameters` of the RollerAgent.
 
 
 Press **Play** to run the scene and use the arrows keys to move the Agent around
diff --git a/docs/Learning-Environment-Design-Agents.md b/docs/Learning-Environment-Design-Agents.md
index 30588fb3b6..f2f49bfe6b 100644
--- a/docs/Learning-Environment-Design-Agents.md
+++ b/docs/Learning-Environment-Design-Agents.md
@@ -17,10 +17,11 @@ discover the optimal decision-making policy.
 The Policy class abstracts out the decision making logic from the Agent itself so
 that you can use the same Policy in multiple Agents. How a Policy makes its
 decisions depends on the kind of Policy it is. You can change the Policy of an
-Agent by changing its `Behavior Parameters`. If you check `Use Heuristic`, the
-Agent will use its `Heuristic()` method to make decisions which can allow you to
-control the Agent manually or write your own Policy. If the Agent has a `Model`
-file, it Policy will use the neural network `Model` to take decisions.
+Agent by changing its `Behavior Parameters`. If you set `Behavior Type` to
+`Heuristic Only`, the Agent will use its `Heuristic()` method to make decisions
+which can allow you to control the Agent manually or write your own Policy. If
+the Agent has a `Model` file, it Policy will use the neural network `Model` to
+take decisions.
 
 ## Decisions
 
diff --git a/docs/Learning-Environment-Examples.md b/docs/Learning-Environment-Examples.md
index a71e9eaec4..9a4ce14952 100644
--- a/docs/Learning-Environment-Examples.md
+++ b/docs/Learning-Environment-Examples.md
@@ -106,8 +106,8 @@ If you would like to contribute environments, please see our
 * Goal: The agents must hit the ball so that the opponent cannot hit a valid
 return.
 * Agents: The environment contains two agent with same Behavior Parameters.
- After training you can check the `Use Heuristic` checkbox on one of the Agents
- to play against your trained model.
+ After training you can set the `Behavior Type` to `Heuristic Only` on one of the Agent's
+ Behavior Parameters to play against your trained model.
 * Agent Reward Function (independent):
   * +1.0 To the agent that wins the point. An agent wins a point by preventing
    the opponent from hitting a valid return.