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Merge pull request #433 from Unity-Technologies/docs-training-brains-etc 

Docs training brains etc
/develop-generalizationTraining-TrainerController
GitHub 7 年前
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共有 17 个文件被更改，包括 443 次插入 和 144 次删除
  1. 11
      docs/Feature-Broadcasting.md
  2. 4
      docs/Feature-Memory.md
  3. 6
      docs/Getting-Started-with-Balance-Ball.md
  4. 27
      docs/Learning-Environment-Create-New.md
  5. 46
      docs/Learning-Environment-Design-Agents.md
  6. 45
      docs/Learning-Environment-Design-Brains.md
  7. 14
      docs/Learning-Environment-Design.md
  8. 6
      docs/Learning-Environment-Examples.md
  9. 18
      docs/Python-API.md
  10. 91
      docs/Training-ML-Agents.md
  11. 16
      docs/Training-PPO.md
  12. 122
      docs/Using-TensorFlow-Sharp-in-Unity.md
  13. 53
      docs/Using-Tensorboard.md
  14. 15
      docs/dox-ml-agents.conf
  15. 62
      docs/Learning-Environment-Design-External-Internal-Brains.md
  16. 22
      docs/Learning-Environment-Design-Heuristic-Brains.md
  17. 29
      docs/Learning-Environment-Design-Player-Brains.md

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# Using the Broadcast Feature
The Player, Heuristic and Internal brains have been updated to support broadcast. The broadcast feature allows you to collect data from your agents in python without controling them.
The Player, Heuristic and Internal brains have been updated to support broadcast. The broadcast feature allows you to collect data from your agents using a Python program without controlling them.
When you launch your Unity Environment from python, you can see what the agents connected to non-external brains are doing. When calling `step` or `reset` on your environment, you retrieve a dictionary from brain names to `BrainInfo` objects. Each `BrainInfo` the non-external brains set to broadcast.
When you launch your Unity Environment from a Python program, you can see what the agents connected to non-external brains are doing. When calling `step` or `reset` on your environment, you retrieve a dictionary mapping brain names to `BrainInfo` objects. The dictionary contains a `BrainInfo` object for each non-external brain set to broadcast as well as for any external brains.
You can use the broadcast feature to collect data generated by Player, Heuristics or Internal brains game sessions. You can then use this data to train an agent in a supervised context.

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# Using Recurrent Neural Network in ML-Agents
# Using Recurrent Neural Networks in ML-Agents
## What are memories for?
Have you ever entered a room to get something and immediately forgot

memory_size: 256
```
* `use_recurent` is a flag that notifies the trainer that you want
* `use_recurrent` is a flag that notifies the trainer that you want
to use a Recurrent Neural Network.
* `sequence_length` defines how long the sequences of experiences
must be while training. In order to use a LSTM, training requires

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* Academy.InitializeAcademy() — Called once when the environment is launched.
* Academy.AcademyStep() — Called at every simulation step before
Agent.AgentAct() (and after the agents collect their observations).
Agent.AgentAction() (and after the agents collect their observations).
* Academy.AcademyReset() — Called when the Academy starts or restarts the
simulation (including the first time).

instance assigned to the agent is set to the continuous vector observation
space with a state size of 8, the `CollectObservations()` must call
`AddVectorObs` 8 times.
* Agent.AgentAct() — Called every simulation step. Receives the action chosen
* Agent.AgentAction() — Called every simulation step. Receives the action chosen
small change in platform rotation at each step. The `AgentAct()` function
small change in platform rotation at each step. The `AgentAction()` function
assigns a reward to the agent; in this example, an agent receives a small
positive reward for each step it keeps the ball on the platform and a larger,
negative reward for dropping the ball. An agent is also marked as done when it

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3. Add one or more Brain objects to the scene as children of the Academy.
4. Implement your Agent subclasses. An Agent subclass defines the code an agent uses to observe its environment, to carry out assigned actions, and to calculate the rewards used for reinforcement training. You can also implement optional methods to reset the agent when it has finished or failed its task.
5. Add your Agent subclasses to appropriate GameObjects, typically, the object in the scene that represents the agent in the simulation. Each Agent object must be assigned a Brain object.
6. If training, set the Brain type to External and [run the training process](Training-PPO.md).
6. If training, set the Brain type to External and [run the training process](Training-ML-Agents.md).
**Note:** If you are unfamiliar with Unity, refer to [Learning the interface](https://docs.unity3d.com/Manual/LearningtheInterface.html) in the Unity Manual if an Editor task isn't explained sufficiently in this tutorial.

2. In the editor, change the base class from `MonoBehaviour` to `Agent`.
3. Delete the `Update()` method, but we will use the `Start()` function, so leave it alone for now.
So far, these are the basic steps that you would use to add ML-Agents to any Unity project. Next, we will add the logic that will let our agent learn to roll to the cube.
So far, these are the basic steps that you would use to add ML-Agents to any Unity project. Next, we will add the logic that will let our agent learn to roll to the cube using reinforcement learning.
In this simple scenario, we don't use the Academy object to control the environment. If we wanted to change the environment, for example change the size of the floor or add or remove agents or other objects before or during the simulation, we could implement the appropriate methods in the Academy. Instead, we will have the Agent do all the work of resetting itself and the target when it succeeds or falls trying.

**Observing the Environment**
The Agent sends the information we collect to the Brain, which uses it to make a decision. When you train the agent using the PPO training algorithm (or use a trained PPO model), the data is fed into a neural network as a feature vector. For an agent to successfully learn a task, we need to provide the correct information. A good rule of thumb for deciding what information to collect is to consider what you would need to calculate an analytical solution to the problem.
The Agent sends the information we collect to the Brain, which uses it to make a decision. When you train the agent (or use a trained model), the data is fed into a neural network as a feature vector. For an agent to successfully learn a task, we need to provide the correct information. A good rule of thumb for deciding what information to collect is to consider what you would need to calculate an analytical solution to the problem.
In our case, the information our agent collects includes:

AddVectorObs(rBody.velocity.z/5);
}
The final part of the Agent code is the Agent.AgentAct() function, which receives the decision from the Brain.
The final part of the Agent code is the Agent.AgentAction() function, which receives the decision from the Brain.
The decision of the Brain comes in the form of an action array passed to the `AgentAct()` function. The number of elements in this array is determined by the `Vector Action Space Type` and `Vector Action Space Size` settings of the agent's Brain. The RollerAgent uses the continuous vector action space and needs two continuous control signals from the brain. Thus, we will set the Brain `Vector Action Size` to 2. The first element,`action[0]` determines the force applied along the x axis; `action[1]` determines the force applied along the z axis. (If we allowed the agent to move in three dimensions, then we would need to set `Vector Action Size` to 3. Note the Brain really has no idea what the values in the action array mean. The training process adjust the action values in response to the observation input and then sees what kind of rewards it gets as a result.
The decision of the Brain comes in the form of an action array passed to the `AgentAction()` function. The number of elements in this array is determined by the `Vector Action Space Type` and `Vector Action Space Size` settings of the agent's Brain. The RollerAgent uses the continuous vector action space and needs two continuous control signals from the brain. Thus, we will set the Brain `Vector Action Size` to 2. The first element,`action[0]` determines the force applied along the x axis; `action[1]` determines the force applied along the z axis. (If we allowed the agent to move in three dimensions, then we would need to set `Vector Action Size` to 3. Note the Brain really has no idea what the values in the action array mean. The training process adjust the action values in response to the observation input and then sees what kind of rewards it gets as a result.
Before we can add a force to the agent, we need a reference to its Rigidbody component. A [Rigidbody](https://docs.unity3d.com/ScriptReference/Rigidbody.html) is Unity's primary element for physics simulation. (See [Physics](https://docs.unity3d.com/Manual/PhysicsSection.html) for full documentation of Unity physics.) A good place to set references to other components of the same GameObject is in the standard Unity `Start()` method:

**Rewards**
Rewards are also assigned in the AgentAct() function. The learning algorithm uses the rewards assigned to the agent property at each step in the simulation and learning process to determine whether it is giving the agent to optimal actions. You want to reward an agent for completing the assigned task (reaching the Target cube, in this case) and punish the agent if it irrevocably fails (falls off the platform). You can sometimes speed up training with sub-rewards that encourage behavior that helps the agent complete the task. For example, the RollerAgent reward system provides a small reward if the agent moves closer to the target in a step.
Reinforcement learning requires rewards. Assign rewards in the `AgentAction()` function. The learning algorithm uses the rewards assigned to the agent at each step in the simulation and learning process to determine whether it is giving the agent the optimal actions. You want to reward an agent for completing the assigned task (reaching the Target cube, in this case) and punish the agent if it irrevocably fails (falls off the platform). You can sometimes speed up training with sub-rewards that encourage behavior that helps the agent complete the task. For example, the RollerAgent reward system provides a small reward if the agent moves closer to the target in a step and a small negative reward at each step which encourages the agent to complete its task quickly.
The RollerAgent calculates the distance to detect when it reaches the target. When it does, the code increments the Agent.reward variable by 1.0 and marks the agent as finished by setting the agent to done.

AddReward(-1.0f);
}
**AgentAct()**
**AgentAction()**
With the action and reward logic outlined above, the final version of the `AgentAct()` function looks like:
With the action and reward logic outlined above, the final version of the `AgentAction()` function looks like:
public override void AgentAct(float[] action)
public override void AgentAction(float[] vectorAction, string textAction)
{
// Rewards
float distanceToTarget = Vector3.Distance(this.transform.position,

// Actions, size = 2
Vector3 controlSignal = Vector3.zero;
controlSignal.x = Mathf.Clamp(action[0], -1, 1);
controlSignal.z = Mathf.Clamp(action[1], -1, 1);
controlSignal.x = Mathf.Clamp(vectorAction[0], -1, 1);
controlSignal.z = Mathf.Clamp(vectorAction[1], -1, 1);
rBody.AddForce(controlSignal * speed);
}

| Element 2 | W | 1 | 1 |
| Element 3 | S | 1 | -1 |
The **Index** value corresponds to the index of the action array passed to `AgentAct()` function. **Value** is assigned to action[Index] when **Key** is pressed.
The **Index** value corresponds to the index of the action array passed to `AgentAction()` function. **Value** is assigned to action[Index] when **Key** is pressed.
Now you can train the Agent. To get ready for training, you must first to change the **Brain Type** from **Player** to **External**. From there the process is the same as described in [Getting Started with the 3D Balance Ball Environment](Getting-Started-with-Balance-Ball.md).
Now you can train the Agent. To get ready for training, you must first to change the **Brain Type** from **Player** to **External**. From there, the process is the same as described in [Training ML-Agents](Training-ML-Agents.md).

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# Agents
An agent is an actor that can observe its environment and decide on the best course of action using those observations. Create agents in Unity by extending the Agent class. The most important aspects of creating agents that can successfully learn are the observations the agent collects and the reward you assign to estimate the value of the agent's current state toward accomplishing its tasks.
An agent is an actor that can observe its environment and decide on the best course of action using those observations. Create agents in Unity by extending the Agent class. The most important aspects of creating agents that can successfully learn are the observations the agent collects and, for reinforcement learning, the reward you assign to estimate the value of the agent's current state toward accomplishing its tasks.
In the ML-Agents framework, an agent passes its observations to its brain at each simulation step. The brain, then, makes a decision and passes the chosen action back to the agent. The agent code executes the action, for example, it moves the agent in one direction or another, and also calculates a reward based on the current state. In training, the reward is used to discover the optimal decision-making policy. (The reward is not used by already trained agents.)
An agent passes its observations to its brain. The brain, then, makes a decision and passes the chosen action back to the agent. Your agent code must execute the action, for example, move the agent in one direction or another. In order to train an agent using [reinforcement learning](Learning-Environment-Design.md), your agent must calculate a reward value at each action. The reward is used to discover the optimal decision-making policy. (A reward is not used by already trained agents or for imitation learning.)
How a brain makes its decisions depends on the type of brain it is. An **External** brain simply passes the observations from its agents to an external process and then passes the decisions made externally back to the agents. During training, the ML-Agents [reinforcement learning](Learning-Environment-Design.md) algorithm adjusts its internal policy parameters to make decisions that optimize the rewards received over time. An Internal brain uses the trained policy parameters to make decisions (and no longer adjusts the parameters in search of a better decision). The other types of brains do not directly involve training, but you might find them useful as part of a training project. See [Brains](Learning-Environment-Design-Brains.md).
How a brain makes its decisions depends on the type of brain it is. An **External** brain simply passes the observations from its agents to an external process and then passes the decisions made externally back to the agents. An **Internal** brain uses the trained policy parameters to make decisions (and no longer adjusts the parameters in search of a better decision). The other types of brains do not directly involve training, but you might find them useful as part of a training project. See [Brains](Learning-Environment-Design-Brains.md).
## Decisions
The observation-decision-action-reward cycle repeats after a configurable number of simulation steps (the frequency defaults to once-per-step). You can also set up an agent to request decisions on demand. Making decisions at regular step intervals is generally most appropriate for physics-based simulations. Making decisions on demand is generally appropriate for situations where agents only respond to specific events or take actions of variable duration. For example, an agent in a robotic simulator that must provide fine-control of joint torques should make its decisions every step of the simulation. On the other hand, an agent that only needs to make decisions when certain game or simulation events occur, should use on-demand decision making.
To control the frequency of step-based decision making, set the **Decision Frequency** value for the Agent object in the Unity Inspector window. Agents using the same Brain instance can use a different frequency. During simulation steps in which no decision is requested, the agent receives the same action chosen by the previous decision.
When you turn on **On Demand Decisions** for an agent, your agent code must call the `Agent.RequestDecision()` function. This function call starts one iteration of the observation-decision-action-reward cycle. The Brain invokes the agent's `CollectObservations()` method, makes a decision and returns it by calling the `AgentAction()` method. The Brain waits for the agent to request the next decision before starting another iteration.
See [On Demand Decision Making](Feature-On-Demand-Decision.md).
* **Continuous** — a feature vector consisting of an array of numbers.
* **Discrete** — an index into a state table (typically only useful for the simplest of environments).
* **Camera** — one or more camera images.
* **Continuous Vector** — a feature vector consisting of an array of numbers.
* **Discrete Vector** — an index into a state table (typically only useful for the simplest of environments).
* **Visual Observations** — one or more camera images.
When you use the **Continuous** or **Discrete** vector observation space for an agent, implement the `Agent.CollectObservations()` method to create the feature vector or state index. When you use camera observations, you only need to identify which Unity Camera objects will provide images and the base Agent class handles the rest. You do not need to implement the `CollectObservations()` method.
When you use the **Continuous** or **Discrete** vector observation space for an agent, implement the `Agent.CollectObservations()` method to create the feature vector or state index. When you use **Visual Observations**, you only need to identify which Unity Camera objects will provide images and the base Agent class handles the rest. You do not need to implement the `CollectObservations()` method when your agent uses visual observations (unless it also uses vector observations).
### Continuous Vector Observation Space: Feature Vectors

## Vector Actions
An action is an instruction from the brain that the agent carries out. The action is passed to the agent as a parameter when the Academy invokes the agent's `AgentAct()` function. When you specify that the vector action space is **Continuous**, the action parameter passed to the agent is an array of control signals with length equal to the `Vector Action Space Size` property. When you specify a **Discrete** vector action space type, the action parameter is an array containing only a single value, which is an index into your list or table of commands. In the **Discrete** vector action space type, the `Vector Action Space Size` is the number of elements in your action table. Set the `Vector Action Space Size` and `Vector Action Space Type` properties on the Brain object assigned to the agent (using the Unity Editor Inspector window).
An action is an instruction from the brain that the agent carries out. The action is passed to the agent as a parameter when the Academy invokes the agent's `AgentAction()` function. When you specify that the vector action space is **Continuous**, the action parameter passed to the agent is an array of control signals with length equal to the `Vector Action Space Size` property. When you specify a **Discrete** vector action space type, the action parameter is an array containing only a single value, which is an index into your list or table of commands. In the **Discrete** vector action space type, the `Vector Action Space Size` is the number of elements in your action table. Set the `Vector Action Space Size` and `Vector Action Space Type` properties on the Brain object assigned to the agent (using the Unity Editor Inspector window).
Neither the Brain nor the training algorithm know anything about what the action values themselves mean. The training algorithm simply tries different values for the action list and observes the affect on the accumulated rewards over time and many training episodes. Thus, the only place actions are defined for an agent is in the `AgentAct()` function. You simply specify the type of vector action space, and, for the continuous vector action space, the number of values, and then apply the received values appropriately (and consistently) in `ActionAct()`.
Neither the Brain nor the training algorithm know anything about what the action values themselves mean. The training algorithm simply tries different values for the action list and observes the affect on the accumulated rewards over time and many training episodes. Thus, the only place actions are defined for an agent is in the `AgentAction()` function. You simply specify the type of vector action space, and, for the continuous vector action space, the number of values, and then apply the received values appropriately (and consistently) in `ActionAct()`.
For example, if you designed an agent to move in two dimensions, you could use either continuous or the discrete vector actions. In the continuous case, you would set the vector action size to two (one for each dimension), and the agent's brain would create an action with two floating point values. In the discrete case, you would set the vector action size to four (one for each direction), and the brain would create an action array containing a single element with a value ranging from zero to four.

### Continuous Action Space
When an agent uses a brain set to the **Continuous** vector action space, the action parameter passed to the agent's `AgentAct()` function is an array with length equal to the Brain object's `Vector Action Space Size` property value. The individual values in the array have whatever meanings that you ascribe to them. If you assign an element in the array as the speed of an agent, for example, the training process learns to control the speed of the agent though this parameter.
When an agent uses a brain set to the **Continuous** vector action space, the action parameter passed to the agent's `AgentAction()` function is an array with length equal to the Brain object's `Vector Action Space Size` property value. The individual values in the array have whatever meanings that you ascribe to them. If you assign an element in the array as the speed of an agent, for example, the training process learns to control the speed of the agent though this parameter.
The [Reacher example](Learning-Environment-Examples.md) defines a continuous action space with four control values.

public override void AgentAct(float[] act)
public override void AgentAction(float[] act)
{
float torque_x = Mathf.Clamp(act[0], -1, 1) * 100f;
float torque_z = Mathf.Clamp(act[1], -1, 1) * 100f;

### Discrete Action Space
When an agent uses a brain set to the **Discrete** vector action space, the action parameter passed to the agent's `AgentAct()` function is an array containing a single element. The value is the index of the action to in your table or list of actions. With the discrete vector action space, `Vector Action Space Size` represents the number of actions in your action table.
When an agent uses a brain set to the **Discrete** vector action space, the action parameter passed to the agent's `AgentAction()` function is an array containing a single element. The value is the index of the action to in your table or list of actions. With the discrete vector action space, `Vector Action Space Size` represents the number of actions in your action table.
The [Area example](Learning-Environment-Examples.md) defines five actions for the discrete vector action space: a jump action and one action for each cardinal direction:

## Rewards
A reward is a signal that the agent has done something right. The PPO reinforcement learning algorithm works by optimizing the choices an agent makes such that the agent earns the highest cumulative reward over time. The better your reward mechanism, the better your agent will learn.
In reinforcement learning, the reward is a signal that the agent has done something right. The PPO reinforcement learning algorithm works by optimizing the choices an agent makes such that the agent earns the highest cumulative reward over time. The better your reward mechanism, the better your agent will learn.
**Note:** Rewards are not used during inference by a brain using an already trained policy and is also not used during imitation learning.
Allocate rewards to an agent by calling the `AddReward()` method in the `AgentAct()` function. The reward assigned in any step should be in the range [-1,1]. Values outside this range can lead to unstable training. The `reward` value is reset to zero at every step.
Allocate rewards to an agent by calling the `AddReward()` method in the `AgentAction()` function. The reward assigned in any step should be in the range [-1,1]. Values outside this range can lead to unstable training. The `reward` value is reset to zero at every step.
You can examine the `AgentAct()` functions defined in the [Examples](Learning-Environment-Examples.md) to see how those projects allocate rewards.
You can examine the `AgentAction()` functions defined in the [Examples](Learning-Environment-Examples.md) to see how those projects allocate rewards.
The `GridAgent` class in the [GridWorld example](Learning-Environment-Examples.md) uses a very simple reward system:

* `Visual Observations` - A list of `Cameras` which will be used to generate observations.
* `Max Step` - The per-agent maximum number of steps. Once this number is reached, the agent will be reset if `Reset On Done` is checked.
* `Reset On Done` - Whether the agent's `AgentReset()` function should be called when the agent reaches its `Max Step` count or is marked as done in code.
* `On Demand Decision` - Whether the agent will request decision at a fixed frequency or if he will be manually have to request decisions with `RequestDecision()`
* Decision Frequency` - If the agent is not `On Demand Decision`, this is the number of steps between decision requests.
* `On Demand Decision` - Whether the agent requests decisions at a fixed step interval or explicitly requests decisions by calling `RequestDecision()`.
* `Decision Frequency` - The number of steps between decision requests. Not used if `On Demand Decision`, is true.
## Instantiating an Agent at Runtime

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The Brain encapsulates the decision making process. Brain objects must be children of the Academy in the Unity scene hierarchy. Every Agent must be assigned a Brain, but you can use the same Brain with more than one Agent. You can also create several Brains, attach each of the Brain to one or more than one Agent.
Use the Brain class directly, rather than a subclass. Brain behavior is determined by the brain type. During training, set your agent's brain type to **External**. You can have multiple brains set to **External** and train them simultaneously. To use the trained model, import the model file into the Unity project and change the brain type to **Internal**. You can extend the CoreBrain class to create different brain types if the four built-in types don't do what you need.
Use the Brain class directly, rather than a subclass. Brain behavior is determined by the **Brain Type**. ML-Agents defines four Brain Types:
* [External](Learning-Environment-External-Internal-Brains.md) — The **External** and **Internal** types typically work together; set **External** when training your agents. You can also use the **External** brain to communicate with a Python script via the Python `UnityEnvironment` class included in the Python portion of the ML-Agents SDK.
* [Internal](Learning-Environment-External-Internal-Brains.md) – Set **Internal** to make use of a trained model.
* [Heuristic](Learning-Environment-Heuristic-Brains.md) – Set **Heuristic** to hand-code the agent's logic by extending the Decision class.
* [Player](Learning-Environment-Player-Brains.md) – Set **Player** to map keyboard keys to agent actions, which can be useful to test your agent code.
During training, set your agent's brain type to **External**. To use the trained model, import the model file into the Unity project and change the brain type to **Internal**.
The Brain Inspector window in the Unity Editor displays the properties assigned to a Brain component:
* `Space Size` - Length of vector observation for brain (In _Continuous_ space type). Or number of possible
values (in _Discrete_ space type).
* `Space Size` - Length of vector observation for brain (In _Continuous_ space type). Or number of possible values (in _Discrete_ space type).
* `Space Size` - Length of action vector for brain (In _Continuous_ state space). Or number of possible
values (in _Discrete_ action space).
* `Space Size` - Length of action vector for brain (In _Continuous_ state space). Or number of possible values (in _Discrete_ action space).
* `External` - Actions are decided using Python API.
* `External` - Actions are decided by an external process, such as the PPO training process.
* `Player` - Actions are decided using Player input mappings.
* `Heuristic` - Actions are decided using custom `Decision` script, which should be attached to the Brain game object.
* `Player` - Actions are decided using keyboard input mappings.
* `Heuristic` - Actions are decided using a custom `Decision` script, which must be attached to the Brain game object.
### Internal Brain
![Internal Brain Inspector](images/internal_brain.png)
* `Graph Model` : This must be the `bytes` file corresponding to the pretrained Tensorflow graph. (You must first drag this file into your Resources folder and then from the Resources folder into the inspector)
* `Graph Scope` : If you set a scope while training your TensorFlow model, all your placeholder name will have a prefix. You must specify that prefix here.
* `Batch Size Node Name` : If the batch size is one of the inputs of your graph, you must specify the name if the placeholder here. The brain will make the batch size equal to the number of agents connected to the brain automatically.
* `Vector Observation Node Name` : If your graph uses a vector observation as an input, you must specify the name if the placeholder here.
* `Recurrent Input Node Name` : If your graph uses a recurrent input / memory as input and outputs new recurrent input / memory, you must specify the name if the input placeholder here.
* `Recurrent Output Node Name` : If your graph uses a recurrent input / memory as input and outputs new recurrent input / memory, you must specify the name if the output placeholder here.
* `Visual Observation Placeholder Name` : If your graph uses observations as input, you must specify it here. Note that the number of observations is equal to the length of `Camera Resolutions` in the brain parameters.
* `Action Node Name` : Specify the name of the placeholder corresponding to the actions of the brain in your graph. If the action space type is continuous, the output must be a one dimensional tensor of float of length `Action Space Size`, if the action space type is discrete, the output must be a one dimensional tensor of int of length 1.
* `Graph Placeholder` : If your graph takes additional inputs that are fixed (example: noise level) you can specify them here. Note that in your graph, these must correspond to one dimensional tensors of int or float of size 1.
* `Name` : Corresponds to the name of the placeholdder.
* `Value Type` : Either Integer or Floating Point.
* `Min Value` and `Max Value` : Specify the range of the value here. The value will be sampled from the uniform distribution ranging from `Min Value` to `Max Value` inclusive.
### Player Brain
![Player Brain Inspector](images/player_brain.png)
If the action space is discrete, you must map input keys to their corresponding integer values. If the action space is continuous, you must map input keys to their corresponding indices and float values.

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Training and simulation proceed in steps orchestrated by the ML-Agents Academy class. The Academy works with Agent and Brain objects in the scene to step through the simulation. When either the Academy has reached its maximum number of steps or all agents in the scene are _done_, one training episode is finished.
During training, the external Python PPO process communicates with the Academy to run a series of episodes while it collects data and optimizes its neural network model. The type of Brain assigned to an agent determines whether it participates in training or not. The **External** brain communicates with the external process to train the TensorFlow model. When training is completed successfully, you can add the trained model file to your Unity project for use with an **Internal** brain.
During training, the external Python training process communicates with the Academy to run a series of episodes while it collects data and optimizes its neural network model. The type of Brain assigned to an agent determines whether it participates in training or not. The **External** brain communicates with the external process to train the TensorFlow model. When training is completed successfully, you can add the trained model file to your Unity project for use with an **Internal** brain.
The ML-Agents Academy class orchestrates the agent simulation loop as follows:

4. Uses each agent's Brain class to decide on the agent's next action.
5. Calls your subclass's `AcademyAct()` function.
6. Calls the `AgentAct()` function for each agent in the scene, passing in the action chosen by the agent's brain. (This function is not called if the agent is done.)
6. Calls the `AgentAction()` function for each agent in the scene, passing in the action chosen by the agent's brain. (This function is not called if the agent is done.)
To create a training environment, extend the Academy and Agent classes to implement the above methods. The `Agent.CollectObservations()` and `Agent.AgentAct()` functions are required; the other methods are optional — whether you need to implement them or not depends on your specific scenario.
To create a training environment, extend the Academy and Agent classes to implement the above methods. The `Agent.CollectObservations()` and `Agent.AgentAction()` functions are required; the other methods are optional — whether you need to implement them or not depends on your specific scenario.
**Note:** The API used by the Python PPO training process to communicate with and control the Academy during training can be used for other purposes as well. For example, you could use the API to use Unity as the simulation engine for your own machine learning algorithms. See [External ML API](Python-API.md) for more information.

* `InitializeAcademy()` — Prepare the environment the first time it launches.
* `AcademyReset()` — Prepare the environment and agents for the next training episode. Use this function to place and initialize entities in the scene as necessary.
* `AcademyStep()` — Prepare the environment for the next simulation step. The base Academy class calls this function before calling any `AgentAct()` methods for the current step. You can use this function to update other objects in the scene before the agents take their actions. Note that the agents have already collected their observations and chosen an action before the Academy invokes this method.
* `AcademyStep()` — Prepare the environment for the next simulation step. The base Academy class calls this function before calling any `AgentAction()` methods for the current step. You can use this function to update other objects in the scene before the agents take their actions. Note that the agents have already collected their observations and chosen an action before the Academy invokes this method.
The base Academy classes also defines several important properties that you can set in the Unity Editor Inspector. For training, the most important of these properties is `Max Steps`, which determines how long each training episode lasts. Once the Academy's step counter reaches this value, it calls the `AcademyReset()` function to start the next episode.

The Agent class represents an actor in the scene that collects observations and carries out actions. The Agent class is typically attached to the GameObject in the scene that otherwise represents the actor — for example, to a player object in a football game or a car object in a vehicle simulation. Every Agent must be assigned a Brain.
To create an agent, extend the Agent class and implement the essential `CollectObservations()` and `AgentAct()` methods:
To create an agent, extend the Agent class and implement the essential `CollectObservations()` and `AgentAction()` methods:
* `AgentAct()` — Carries out the action chosen by the agent's brain and assigns a reward to the current state.
* `AgentAction()` — Carries out the action chosen by the agent's brain and assigns a reward to the current state.
You must also determine how an Agent finishes its task or times out. You can manually set an agent to done in your `AgentAct()` function when the agent has finished (or irrevocably failed) its task. You can also set the agent's `Max Steps` property to a positive value and the agent will consider itself done after it has taken that many steps. When the Academy reaches its own `Max Steps` count, it starts the next episode. If you set an agent's `ResetOnDone` property to true, then the agent can attempt its task several times in one episode. (Use the `Agent.AgentReset()` function to prepare the agent to start again.)
You must also determine how an Agent finishes its task or times out. You can manually set an agent to done in your `AgentAction()` function when the agent has finished (or irrevocably failed) its task. You can also set the agent's `Max Steps` property to a positive value and the agent will consider itself done after it has taken that many steps. When the Academy reaches its own `Max Steps` count, it starts the next episode. If you set an agent's `ResetOnDone` property to true, then the agent can attempt its task several times in one episode. (Use the `Agent.AgentReset()` function to prepare the agent to start again.)
See [Agents](Learning-Environment-Design-Agents.md) for detailed information about programing your own agents.

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Unity ML-Agents contains an expanding set of example environments which
demonstrate various features of the platform. Environments are located in
`unity-environment/Assets/ML-Agents/Examples` and summarised below.
`unity-environment/Assets/ML-Agents/Examples` and summarized below.
Additionally, our
[first ML Challenge](https://connect.unity.com/challenges/ml-agents-1)
contains environments created by the community.

* Agent Reward Function (independent):
* +0.1 Each step agent's hand is in goal location.
* Brains: One brain with the following observation/action space.
* Vector Observation space: (Continuous) 26 variables corresponding to position, rotation, velocity, and angular velocities of the two arm rigidbodies.
* Vector Observation space: (Continuous) 26 variables corresponding to position, rotation, velocity, and angular velocities of the two arm Rigidbodies.
* Vector Action space: (Continuous) Size of 4, corresponding to torque applicable to two joints.
* Visual Observations: None
* Reset Parameters: Two, corresponding to goal size, and goal movement speed.

![Hallway](images/hallway.png)
* Set-up: Environment where the agent needs to find information in a room, remeber it, and use it to move to the correct goal.
* Set-up: Environment where the agent needs to find information in a room, remember it, and use it to move to the correct goal.
* Goal: Move to the goal which corresponds to the color of the block in the room.
* Agents: The environment contains one agent linked to a single brain.
* Agent Reward Function (independent):

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# Python API
ML-Agents provides a Python API for controlling the agent simulation loop of a environment or game built with Unity. This API is used by the ML-Agent training algorithms (run with `learn.py`), but you can also write your Python programs using this API.
The key objects in the Python API include:
* **UnityEnvironment** — the main interface between the Unity application and your code. Use UnityEnvironment to start and control a simulation or training session.
* **BrainInfo** — contains all the data from agents in the simulation, such as observations and rewards.
* **BrainParameters** — describes the data elements in a BrainInfo object. For example, provides the array length of an observation in BrainInfo.
These classes are all defined in the `python/unityagents` folder of the ML-Agents SDK.
To communicate with an agent in a Unity environment from a Python program, the agent must either use an **External** brain or use a brain that is broadcasting (has its **Broadcast** property set to true). Your code is expected to return actions for agents with external brains, but can only observe broadcasting brains (the information you receive for an agent is the same in both cases). See [Using the Broadcast Feature](Feature-Broadcast.md).
For a simple example of using the Python API to interact with a Unity environment, see the Basic [Jupyter](Background-Jupyter.md) notebook, which opens an environment, runs a few simulation steps taking random actions, and closes the environment.
Python-side communication happens through `UnityEnvironment` which is located in `python/unityagents`. To load a Unity environment from a built binary file, put the file in the same directory as `unityagents`. If your filename is 3DBall.app, in python, run:
Python-side communication happens through `UnityEnvironment` which is located in `python/unityagents`. To load a Unity environment from a built binary file, put the file in the same directory as `unityagents`. For example, if the filename of your Unity environment is 3DBall.app, in python, run:
```python
from unityagents import UnityEnvironment

* **`agents`** : A list of the unique ids of the agents using the brain.
* **`previous_actions`** : A two dimensional numpy array of dimension `(batch size, vector action size)` if the vector action space is continuous and `(batch size, 1)` if the vector action space is discrete.
Once loaded, `env` can be used in the following way:
Once loaded, you can use your UnityEnvironment object, which referenced by a variable named `env` in this example, can be used in the following way:
- **Print : `print(str(env))`**
Prints all parameters relevant to the loaded environment and the external brains.
- **Reset : `env.reset(train_model=True, config=None)`**

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# Training ML-Agents
This document is still to be written. When finished it will provide an overview of the training process. The main algorithm implemented currently is PPO, but there are various flavors including multi-agent training, curriculum training and imitation learning to consider.
ML-Agents conducts training using an external Python training process. During training, this external process communicates with the Academy object in the Unity scene to generate a block of agent experiences. These experiences become the training set for a neural network used to optimize the agent's policy (which is essentially a mathematical function mapping observations to actions). In reinforcement learning, the neural network optimizes the policy by maximizing the expected rewards. In imitation learning, the neural network optimizes the policy to achieve the smallest difference between the actions chosen by the agent trainee and the actions chosen by the expert in the same situation.
The output of the training process is a model file containing the optimized policy. This model file is a TensorFlow data graph containing the mathematical operations and the optimized weights selected during the training process. You can use the generated model file with the Internal Brain type in your Unity project to decide the best course of action for an agent.
Use the Python program, `learn.py` to train your agents. This program can be found in the `python` directory of the ML-Agents SDK. The [configuration file](#training-config-file), `trainer_config.yaml` specifies the hyperparameters used during training. You can edit this file with a text editor to add a specific configuration for each brain.
For a broader overview of reinforcement learning, imitation learning and the ML-Agents training process, see [ML-Agents Overview](ML-Agents-Overview.md).
## Training with Learn.py
Use the Python `Learn.py` program to train agents. `Learn.py` supports training with [reinforcement learning](Background-Machine-Learning.md#reinforcement-learning), [curriculum learning](Training-Curriculum-Learning.md), and [behavioral cloning imitation learning](Training-Imitation-Learning.md).
Run `Learn.py` from the command line to launch the training process. Use the command line patterns and the `trainer_config.yaml` file to control training options.
The basic command for training is:
python learn.py <env_file_path> --run-id=<run-identifier> --train
where `<env_file_path>` is the path to your Unity executable containing the agents to be trained and `<run-identifier>` is an optional identifier you can use to identify the results of individual training runs.
For example, suppose you have a project in Unity named "CatsOnBicyclesCatsOnBicycles" which contains agents ready to train. To perform the training:
1. Build the project, making sure that you only include the training scene.
2. Open a terminal or console window.
3. Navigate to the ml-agents `python` folder.
4. Run the following to launch the training process using the path to the Unity environment you built in step 1:
python learn.py ../../projects/Cats/CatsOnBicycles.app --run-id=cob_1 --train
During a training session, the training program prints out and saves updates at regular intervals (specified by the `summary_freq` option). The saved statistics are grouped by the `run-id` value so you should assign a unique id to each training run if you plan to view the statistics. You can view these statistics using TensorBoard during or after training by running the following command (from the ML-Agents python directory):
tensorboard --logdir=summaries
And then opening the URL: [localhost:6006](http://localhost:6006).
When training is finished, you can find the saved model in the `python/models` folder under the assigned run-id — in the cats example, the path to the model would be `python/models/cob_1/CatsOnBicycles_cob_1.bytes`.
While this example used the default training hyperparameters, you can edit the [training_config.yaml file](#training-config-file) with a text editor to set different values.
### Command line training options
In addition to passing the path of the Unity executable containing your training environment, you can set the following command line options when invoking `learn.py`:
* `--curriculum=<file>` – Specify a curriculum json file for defining the lessons for curriculum training. See [Curriculum Training](Training-Curriculum-Learning.md) for more information.
* `--keep-checkpoints=<n>` – Specify the maximum number of model checkpoints to keep. Checkpoints are saved after the number of steps specified by the `save-freq` option. Once the maximum number of checkpoints has been reached, the oldest checkpoint is deleted when saving a new checkpoint. Defaults to 5.
* `--lesson=<n>` – Specify which lesson to start with when performing curriculum training. Defaults to 0.
* `--load` – If set, the training code loads an already trained model to initialize the neural network before training. The learning code looks for the model in `python/models/<run-id>/` (which is also where it saves models at the end of training). When not set (the default), the neural network weights are randomly initialized and an existing model is not loaded.
* `--run-id=<path>` – Specifies an identifier for each training run. This identifier is used to name the subdirectories in which the trained model and summary statistics are saved as well as the saved model itself. The default id is "ppo". If you use TensorBoard to view the training statistics, always set a unique run-id for each training run. (The statistics for all runs with the same id are combined as if they were produced by a the same session.)
* `--save-freq=<n>` Specifies how often (in steps) to save the model during training. Defaults to 50000.
* `--seed=<n>` – Specifies a number to use as a seed for the random number generator used by the training code.
* `--slow` – Specify this option to run the Unity environment at normal, game speed. The `--slow` mode uses the **Time Scale** and **Target Frame Rate** specified in the Academy's **Inference Configuration**. By default, training runs using the speeds specified in your Academy's **Training Configuration**. See [Academy Properties](Learning-Environment-Design-Academy.md#academy-properties).
* `--train` – Specifies whether to train model or only run in inference mode. When training, **always** use the `--train` option.
* `--worker-id=<n>` – When you are running more than one training environment at the same time, assign each a unique worker-id number. The worker-id is added to the communication port opened between the current instance of learn.py and the ExternalCommunicator object in the Unity environment. Defaults to 0.
* `--docker-target-name=<dt>` – The Docker Volume on which to store curriculum, executable and model files. See [Using Docker](Using-Docker.md).
### Training config file
The training config file, `trainer_config.yaml` specifies the training method, the hyperparameters, and a few additional values to use during training. The file is divided into sections. The **default** section defines the default values for all the available settings. You can also add new sections to override these defaults to train specific Brains. Name each of these override sections after the GameObject containing the Brain component that should use these settings. (This GameObject will be a child of the Academy in your scene.) Sections for the example environments are included in the provided config file. `Learn.py` finds the config file by name and looks for it in the same directory as itself.
| ** Setting ** | **Description** | **Applies To Trainer**|
| :-- | :-- | :-- |
| batch_size | The number of experiences in each iteration of gradient descent.| PPO, BC |
| batches_per_epoch | In imitation learning, the number of batches of training examples to collect before training the model.| BC |
| beta | The strength of entropy regularization.| PPO, BC |
| brain_to_imitate | For imitation learning, the name of the GameObject containing the Brain component to imitate. | BC |
| buffer_size | The number of experiences to collect before updating the policy model. | PPO, BC |
| epsilon | Influences how rapidly the policy can evolve during training.| PPO, BC |
| gamma | The reward discount rate for the Generalized Advantage Estimator (GAE). | PPO |
| hidden_units | The number of units in the hidden layers of the neural network. | PPO, BC |
| lambd | The regularization parameter. | PPO |
| learning_rate | The initial learning rate for gradient descent. | PPO, BC |
| max_steps | The maximum number of simulation steps to run during a training session. | PPO, BC |
| memory_size | The size of the memory an agent must keep. Used for training with a recurrent neural network. See [Using Recurrent Neural Networks in ML-Agents](Feature-Memory.md). | PPO, BC |
| normalize | Whether to automatically normalize observations. | PPO, BC |
| num_epoch | The number of passes to make through the experience buffer when performing gradient descent optimization. | PPO, BC |
| num_layers | The number of hidden layers in the neural network. | PPO, BC |
| sequence_length | Defines how long the sequences of experiences must be while training. Only used for training with a recurrent neural network. See [Using Recurrent Neural Networks in ML-Agents](Feature-Memory.md). | PPO, BC |
| summary_freq | How often, in steps, to save training statistics. This determines the number of data points shown by TensorBoard. | PPO, BC |
| time_horizon | How many steps of experience to collect per-agent before adding it to the experience buffer. | PPO, BC |
| trainer | The type of training to perform: "ppo" or "imitation".| PPO, BC |
| use_recurrent | Train using a recurrent neural network. See [Using Recurrent Neural Networks in ML-Agents](Feature-Memory.md).| PPO, BC |
|| PPO = Proximal Policy Optimization, BC = Behavioral Cloning (Imitation)) ||
For specific advice on setting hyperparameters based on the type of training you are conducting, see:
* [Training with PPO](Training-PPO.md)
* [Using Recurrent Neural Networks in ML-Agents](Feature-Memory.md)
* [Imitation Learning](Training-Imitation-Learning.md)
* [Training with Curriculum Learning](Training-Curriculum-Learning.md)
You can also compare the [example environments](Learning-Environment-Examples.md) to the corresponding sections of the `trainer-config.yaml` file for each example to see how the hyperparameters and other configuration variables have been changed from the defaults.

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# Training with Proximal Policy Optimization
This section is still to be written. Refer to [Getting Started with the 3D Balance Ball Environment](Getting-Started-with-Balance-Ball.md) for a walk-through of the PPO training process.
ML-Agents uses a reinforcement learning technique called [Proximal Policy Optimization (PPO)](https://blog.openai.com/openai-baselines-ppo/). PPO uses a neural network to approximate the ideal function that maps an agent's observations to the best action an agent can take in a given state. The ML-Agents PPO algorithm is implemented in TensorFlow and runs in a separate Python process (communicating with the running Unity application over a socket).
See [Training ML-Agents](Training ML-Agents.md) for instructions on running the training program, `learn.py`.
If you are using the recurrent neural network (RNN) to utilize memory, see [Using Recurrent Neural Networks in ML-Agents](Feature-Memory.md) for RNN-specific training details.
If you are using curriculum training to pace the difficulty of the learning task presented to an agent, see [Training with Curriculum Learning](Training-Curriculum-Learning.md).
For information about imitation learning, which uses a different training algorithm, see [Imitation Learning](Training-Imitation-Learning).
<!-- Need a description of PPO that provides a general overview of the algorithm and, more specifically, puts all the hyperparameters and Academy/Brain/Agent settings (like max_steps and done) into context. Oh, and which is also short and understandable by laymen. -->
The process of training a Reinforcement Learning model can often involve the need to tune the hyperparameters in order to achieve
a level of performance that is desirable. This guide contains some best practices for tuning the training process when the default
parameters don't seem to be giving the level of performance you would like.
Successfully training a Reinforcement Learning model often involves tuning the training hyperparameters. This guide contains some best practices for tuning the training process when the default parameters don't seem to be giving the level of performance you would like.
### Hyperparameters

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# Using TensorFlowSharp in Unity _[Experimental]_
Unity now offers the possibility to use pre-trained Tensorflow graphs inside of the game engine. This was made possible thanks to [the TensorFlowSharp project](https://github.com/migueldeicaza/TensorFlowSharp).
ML-Agents allows you to use pre-trained [TensorFlow graphs](https://www.tensorflow.org/programmers_guide/graphs) inside your Unity games. This support is possible thanks to [the TensorFlowSharp project](https://github.com/migueldeicaza/TensorFlowSharp). The primary purpose for this support is to use the TensorFlow models produced by the ML-Agents own training programs, but a side benefit is that you can use any TensorFlow model.
_Notice: This feature is still experimental. While it is possible to embed trained models into Unity games, Unity Technologies does not officially support this use-case for production games at this time. As such, no guarantees are provided regarding the quality of experience. If you encounter issues regarding battery life, or general performance (especially on mobile), please let us know._

* Mac OSX 64 bits
* Mac OS X 64 bits
* Windows 64 bits
* iOS (Requires additional steps)
* Android

* Unity 2017.1 or above
* Unity Tensorflow Plugin ([Download here](https://s3.amazonaws.com/unity-agents/0.2/TFSharpPlugin.unitypackage))
* Unity TensorFlow Plugin ([Download here](https://s3.amazonaws.com/unity-agents/0.3/TFSharpPlugin.unitypackage))
In order to bring a fully trained agent back into Unity, you will need to make sure the nodes of your graph have appropriate names. You can give names to nodes in Tensorflow :
Go to `Edit` -> `Player Settings` and add `ENABLE_TENSORFLOW` to the `Scripting Define Symbols` for each type of device you want to use (**`PC, Mac and Linux Standalone`**, **`iOS`** or **`Android`**).
Set the Brain you used for training to `Internal`. Drag `your_name_graph.bytes` into Unity and then drag it into The `Graph Model` field in the Brain.
## Using your own trained graphs
The TensorFlow data graphs produced by the ML-Agents training programs work without any additional settings.
In order to use a TensorFlow data graph in Unity, make sure the nodes of your graph have appropriate names. You can assign names to nodes in TensorFlow :
We recommend using the following naming convention:
We recommend using the following naming conventions:
* Name the batch size input placeholder `batch_size`
* Name the input vector observation placeholder `state`
* Name the output node `action`

You can have additional placeholders for float or integers but they must be placed in placeholders of dimension 1 and size 1. (Be sure to name them)
You can have additional placeholders for float or integers but they must be placed in placeholders of dimension 1 and size 1. (Be sure to name them.)
It is important that the inputs and outputs of the graph are exactly the one you receive / give when training your model with an `External` brain. This means you cannot have any operations such as reshaping outside of the graph.
It is important that the inputs and outputs of the graph are exactly the ones you receive and return when training your model with an `External` brain. This means you cannot have any operations such as reshaping outside of the graph.
While training your Agent using the Python API, you can save your graph at any point of the training. Note that the argument `output_node_names` must be the name of the tensor your graph outputs (separated by a coma if multiple outputs). In this case, it will be either `action` or `action,recurrent_out` if you have recurrent outputs.
While training your Agent using the Python API, you can save your graph at any point of the training. Note that the argument `output_node_names` must be the name of the tensor your graph outputs (separated by a coma if using multiple outputs). In this case, it will be either `action` or `action,recurrent_out` if you have recurrent outputs.
```python
from tensorflow.python.tools import freeze_graph

clear_devices = True, initializer_nodes = "",input_saver = "",
restore_op_name = "save/restore_all", filename_tensor_name = "save/Const:0")
```
Your model will be saved with the name `your_name_graph.bytes` and will contain both the graph and associated weights. Note that you must save your graph as a bytes file so Unity can load it.
Your model will be saved with the name `your_name_graph.bytes` and will contain both the graph and associated weights. Note that you must save your graph as a .bytes file so Unity can load it.
In the Unity Editor, you must specify the names of the nodes used by your graph in the **Internal** brain Inspector window. If you used a scope when defining your graph, specify it in the `Graph Scope` field.
## Inside Unity
![Internal Brain Inspector](images/internal_brain.png)
Go to `Edit` -> `Player Settings` and add `ENABLE_TENSORFLOW` to the `Scripting Define Symbols` for each type of device you want to use (**`PC, Mac and Linux Standalone`**, **`iOS`** or **`Android`**).
See [Internal Brain](Learning-Environments-Internal-Brains.md) for more information about using the InternalBrain object.
Set the Brain you used for training to `Internal`. Drag `your_name_graph.bytes` into Unity and then drag it into The `Graph Model` field in the Brain. If you used a scope when training you graph, specify it in the `Graph Scope` field. Specify the names of the nodes you used in your graph. If you followed these instructions well, the agents in your environment that use this brain will use you fully trained network to make decisions.
If you followed these instructions well, the agents in your environment that use this brain will use your fully trained network to make decisions.
# iOS additional instructions for building

# Using TensorFlowSharp without ML-Agents
Beyond controlling an in-game agent, you may desire to use TensorFlowSharp for more general computation. The below instructions describe how to generally embed Tensorflow models without using the ML-Agents framework.
Beyond controlling an in-game agent, you can also use TensorFlowSharp for more general computation. The following instructions describe how to generally embed TensorFlow models without using the ML-Agents framework.
You must have a Tensorflow graph `your_name_graph.bytes` made using Tensorflow's `freeze_graph.py`. The process to create such graph is explained above.
You must have a TensorFlow graph, such as `your_name_graph.bytes`, made using TensorFlow's `freeze_graph.py`. The process to create such graph is explained in[Using your own trained graphs](#using-your-own-trained-graphs).
Put the file `your_name_graph.bytes` into Resources.
To load and use a TensorFlow data graph in Unity:
In your C# script :
At the top, add the line
```csharp
using TensorFlow;
```
If you will be building for android, you must add this block at the start of your code :
```csharp
#if UNITY_ANDROID
TensorFlowSharp.Android.NativeBinding.Init();
#endif
```
Put your graph as a text asset in the variable `graphModel`. You can do so in the inspector by making `graphModel` a public variable and dragging you asset in the inspector or load it from the Resources folder :
```csharp
TextAsset graphModel = Resources.Load (your_name_graph) as TextAsset;
```
You then must recreate the graph in Unity by adding the code :
```csharp
graph = new TFGraph ();
graph.Import (graphModel.bytes);
session = new TFSession (graph);
```
Your newly created graph need to get input tensors. Here is an example with a one dimensional tensor of size 2:
1. Put the file, `your_name_graph.bytes`, into Resources.
2. At the top off your C# script, add the line:
```csharp
using TensorFlow;
```
3. If you will be building for android, you must add this block at the start of your code :
```csharp
#if UNITY_ANDROID
TensorFlowSharp.Android.NativeBinding.Init();
#endif
```
4. Load your graph as a text asset into a variable, such as `graphModel`:
```csharp
TextAsset graphModel = Resources.Load (your_name_graph) as TextAsset;
```
5. You then must instantiate the graph in Unity by adding the code :
```csharp
graph = new TFGraph ();
graph.Import (graphModel.bytes);
session = new TFSession (graph);
```
6. Assign the input tensors for the graph. For example, the following code assigns a one dimensional input tensor of size 2:
```csharp
var runner = session.GetRunner ();
runner.AddInput (graph ["input_placeholder_name"] [0], new float[]{ placeholder_value1, placeholder_value2 });
```
You need to give all required inputs to the graph. There is one input per TensorFlow placeholder.
```csharp
var runner = session.GetRunner ();
runner.AddInput (graph ["input_placeholder_name"] [0], new float[]{ placeholder_value1, placeholder_value2 });
```
To retrieve the output of your graph run the following code. Note that this is for an output that would be a two dimensional tensor of floats. Cast to a long array if your outputs are integers.
```csharp
runner.Fetch (graph["output_placeholder_name"][0]);
float[,] recurrent_tensor = runner.Run () [0].GetValue () as float[,];
```
You must provide all required inputs to the graph. Supply one input per TensorFlow placeholder.
7. To calculate and access the output of your graph, run the following code.
```csharp
runner.Fetch (graph["output_placeholder_name"][0]);
float[,] recurrent_tensor = runner.Run () [0].GetValue () as float[,];
```
Note that this example assumes the output array is a two-dimensional tensor of floats. Cast to a long array if your outputs are integers.

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# Using TensorBoard to Observe Training
This document is still to be written. It will discuss using TensorBoard and interpreting the TensorBoard charts.
ML-Agents saves statistics during learning session that you can view with a TensorFlow utility named, [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard).
The `learn.py` program saves training statistics to a folder named `summaries`, organized by the `run-id` value you assign to a training session.
In order to observe the training process, either during training or afterward,
start TensorBoard:
1. Open a terminal or console window:
2. Navigate to the ml-agents/python folder.
3. From the command line run :
tensorboard --logdir=summaries
4. Open a browser window and navigate to [localhost:6006](http://localhost:6006).
**Note:** If you don't assign a `run-id` identifier, `learn.py` uses the default string, "ppo". All the statistics will be saved to the same sub-folder and displayed as one session in TensorBoard. After a few runs, the displays can become difficult to interpret in this situation. You can delete the folders under the `summaries` directory to clear out old statistics.
On the left side of the TensorBoard window, you can select which of the training runs you want to display. You can select multiple run-ids to compare statistics. The TensorBoard window also provides options for how to display and smooth graphs.
When you run the training program, `learn.py`, you can use the `--save-freq` option to specify how frequently to save the statistics.
## ML-Agents training statistics
The ML-agents training program saves the following statistics:
* Lesson - Plots the progress from lesson to lesson. Only interesting when performing
[curriculum training](Training-Curriculum-Learning.md).
* Cumulative Reward - The mean cumulative episode reward over all agents.
Should increase during a successful training session.
* Entropy - How random the decisions of the model are. Should slowly decrease
during a successful training process. If it decreases too quickly, the `beta`
hyperparameter should be increased.
* Episode Length - The mean length of each episode in the environment for all
agents.
* Learning Rate - How large a step the training algorithm takes as it searches
for the optimal policy. Should decrease over time.
* Policy Loss - The mean loss of the policy function update. Correlates to how
much the policy (process for deciding actions) is changing. The magnitude of
this should decrease during a successful training session.
* Value Estimate - The mean value estimate for all states visited by the agent.
Should increase during a successful training session.
* Value Loss - The mean loss of the value function update. Correlates to how
well the model is able to predict the value of each state. This should decrease
during a successful training session.

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docs/dox-ml-agents.conf
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# Doxyfile 1.8.13
# This file describes the settings to be used by the documentation system
# doxygen (www.doxygen.org) for a project.
# To generate the C# API documentation, run:
#
# doxygen dox-ml-agents.conf
# All text after a double hash (##) is considered a comment and is placed in
# front of the TAG it is preceding.
#
# All text after a single hash (#) is considered a comment and will be ignored.
# The format is:
# TAG = value [value, ...]
# For lists, items can also be appended using:
# TAG += value [value, ...]
# Values that contain spaces should be placed between quotes (\" \").
# from the ml-agents-docs directory
#---------------------------------------------------------------------------
# Project related configuration options

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docs/Learning-Environment-Design-External-Internal-Brains.md
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# External and Internal Brains
The **External** and **Internal** types of Brains work in different phases of training. When training your agents, set their brain types to **External**; when using the trained models, set their brain types to **Internal**.
## External Brain
When [running an ML-Agents training algorithm](Training-ML-Agents.md), at least one Brain object in a scene must be set to **External**. This allows the training process to collect the observations of agents using that brain and give the agents their actions.
In addition to using an External brain for training using the ML-Agents learning algorithms, you can use an External brain to control agents in a Unity environment using an external Python program. See [Python API](Python-API.md) for more information.
Unlike the other types, the External Brain has no properties to set in the Unity Inspector window.
## Internal Brain
The Internal Brain type uses a [TensorFlow model](https://www.tensorflow.org/get_started/get_started_for_beginners#models_and_training) to make decisions. The Proximal Policy Optimization (PPO) and Behavioral Cloning algorithms included with the ML-Agents SDK produce trained TensorFlow models that you can use with the Internal Brain type.
A __model__ is a mathematical relationship mapping an agent's observations to its actions. TensorFlow is a software library for performing numerical computation through data flow graphs. A TensorFlow model, then, defines the mathematical relationship between your agent's observations and its actions using a TensorFlow data flow graph.
### Creating a graph model
The training algorithms included in the ML-Agents SDK produce TensorFlow graph models as the end result of the training process. See [Training ML-Agents](Training-ML-Agents.md) for instructions on how to train a model.
### Using a graph model
To use a graph model:
1. Select the Brain GameObject in the **Hierarchy** window of the Unity Editor. (The Brain GameObject must be a child of the Academy Gameobject and must have a Brain component.)
2. Set the **Brain Type** to **Internal**.
**Note:** In order to see the **Internal** Brain Type option, you must [enable TensorFlowSharp](Using-TensorFlow-Sharp-in-Unity.md).
3. Import the `environment_run-id.bytes` file produced by the PPO training program. (Where `environment_run-id` is the name of the model file, which is constructed from the name of your Unity environment executable and the run-id value you assigned when running the training process.)
You can [import assets into Unity](https://docs.unity3d.com/Manual/ImportingAssets.html) in various ways. The easiest way is to simply drag the file into the **Project** window and drop it into an appropriate folder.
4. Once the `environment.bytes` file is imported, drag it from the **Project** window to the **Graph Model** field of the Brain component.
If you are using a model produced by the ML-Agents `learn.py` program, use the default values for the other Internal Brain parameters.
### Internal Brain properties
The default values of the TensorFlow graph parameters work with the model produced by the PPO and BC training code in the ML-Agents SDK. To use a default ML-Agents model, the only parameter that you need to set is the `Graph Model`, which must be set to the .bytes file containing the trained model itself.
![Internal Brain Inspector](images/internal_brain.png)
* `Graph Model` : This must be the `bytes` file corresponding to the pretrained Tensorflow graph. (You must first drag this file into your Resources folder and then from the Resources folder into the inspector)
Only change the following Internal Brain properties if you have created your own TensorFlow model and are not using an ML-Agents model:
* `Graph Scope` : If you set a scope while training your TensorFlow model, all your placeholder name will have a prefix. You must specify that prefix here.
* `Batch Size Node Name` : If the batch size is one of the inputs of your graph, you must specify the name if the placeholder here. The brain will make the batch size equal to the number of agents connected to the brain automatically.
* `State Node Name` : If your graph uses the state as an input, you must specify the name of the placeholder here.
* `Recurrent Input Node Name` : If your graph uses a recurrent input / memory as input and outputs new recurrent input / memory, you must specify the name if the input placeholder here.
* `Recurrent Output Node Name` : If your graph uses a recurrent input / memory as input and outputs new recurrent input / memory, you must specify the name if the output placeholder here.
* `Observation Placeholder Name` : If your graph uses observations as input, you must specify it here. Note that the number of observations is equal to the length of `Camera Resolutions` in the brain parameters.
* `Action Node Name` : Specify the name of the placeholder corresponding to the actions of the brain in your graph. If the action space type is continuous, the output must be a one dimensional tensor of float of length `Action Space Size`, if the action space type is discrete, the output must be a one dimensional tensor of int of length 1.
* `Graph Placeholder` : If your graph takes additional inputs that are fixed (example: noise level) you can specify them here. Note that in your graph, these must correspond to one dimensional tensors of int or float of size 1.
* `Name` : Corresponds to the name of the placeholder.
* `Value Type` : Either Integer or Floating Point.
* `Min Value` and `Max Value` : Specify the range of the value here. The value will be sampled from the uniform distribution ranging from `Min Value` to `Max Value` inclusive.

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docs/Learning-Environment-Design-Heuristic-Brains.md
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# 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 agent's 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.

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docs/Learning-Environment-Design-Player-Brains.md
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# Player Brain
The **Player** brain type allows you to control an agent using keyboard commands. You can use Player brains to control a "teacher" agent that trains other agents during [imitation learning](Training-Imitation-Learning.md). You can also use Player brains to test your agents and environment before changing their brain types to **External** and running the training process.
## Player Brain properties
The **Player** brain properties allow you to assign one or more keyboard keys to each action and a unique value to send when a key is pressed.
![Player Brain Inspector](images/player_brain.png)
Note the differences between the discrete and continuous action spaces. When a brain uses the discrete action space, you can send one integer value as the action per step. In contrast, when a brain uses the continuous action space you can send any number of floating point values (up to the **Vector Action Space Size** setting).
| **Property** | | **Description** |
| :-- |:-- | :-- |
|**Continuous Player Actions**|| The mapping for the continuous vector action space. Shown when the action space is **Continuous**|.
|| **Size** | The number of key commands defined. You can assign more than one command to the same action index in order to send different values for that action. (If you press both keys at the same time, deterministic results are not guaranteed.)|
||**Element 0–N**| The mapping of keys to action values. |
|| **Key** | The key on the keyboard. |
|| **Index** | The element of the agent's action vector to set when this key is pressed. The index value cannot exceed the size of the Action Space (minus 1, since it is an array index).|
|| **Value** | The value to send to the agent as its action for the specified index when the mapped key is pressed. All other members of the action vector are set to 0. |
|**Discrete Player Actions**|| The mapping for the discrete vector action space. Shown when the action space is **Discrete**.|
|| **Default Action** | The value to send when no keys are pressed.|
|| **Size** | The number of key commands defined. |
||**Element 0–N**| The mapping of keys to action values. |
|| **Key** | The key on the keyboard. |
|| **Value** | The value to send to the agent as its action when the mapped key is pressed.|
For more information about the Unity input system, see [Input](https://docs.unity3d.com/ScriptReference/Input.html).
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