# Example Learning Environments

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 summarized below. 
Additionally, our 
[first ML Challenge](https://connect.unity.com/challenges/ml-agents-1)
contains environments created by the community.

This page only overviews the example environments we provide. To learn more
on how to design and build your own environments see our 
[Making a New Learning Environment](Learning-Environment-Create-New.md)
page.

Note: Environment scenes marked as _optional_ do not have accompanying pre-trained model files, and are designed to serve as challenges for researchers. 

If you would like to contribute environments, please see our 
[contribution guidelines](../CONTRIBUTING.md) page. 

## Basic

![Basic](images/basic.png)

* Set-up: A linear movement task where the agent must move left or right to rewarding states.
* Goal: Move to the most reward state.
* Agents: The environment contains one agent linked to a single brain.
* Agent Reward Function: 
    * +0.1 for arriving at suboptimal state.
    * +1.0 for arriving at optimal state.
* Brains: One brain with the following observation/action space.
    * Vector Observation space: (Discrete) One variable corresponding to current state.
    * Vector Action space: (Discrete) Two possible actions (Move left, move right).
    * Visual Observations: None.
* Reset Parameters: None
* Benchmark Mean Reward: 0.94

## [3DBall: 3D Balance Ball](https://youtu.be/dheeCO29-EI)

![3D Balance Ball](images/balance.png)

* Set-up: A balance-ball task, where the agent controls the platform. 
* Goal: The agent must balance the platform in order to keep the ball on it for as long as possible.
* Agents: The environment contains 12 agents of the same kind, all linked to a single brain.
* Agent Reward Function: 
    * +0.1 for every step the ball remains on the platform. 
    * -1.0 if the ball falls from the platform.
* Brains: One brain with the following observation/action space.
    * Vector Observation space: (Continuous) 8 variables corresponding to rotation of platform, and position, rotation, and velocity of ball.
    * Vector Observation space (Hard Version): (Continuous) 5 variables corresponding to rotation of platform and position and rotation of ball.
    * Vector Action space: (Continuous) Size of 2, with one value corresponding to X-rotation, and the other to Z-rotation.
    * Visual Observations: None.
* Reset Parameters: None
* Benchmark Mean Reward: 100

## [GridWorld](https://youtu.be/gu8HE9WKEVI)

![GridWorld](images/gridworld.png)

* Set-up: A version of the classic grid-world task. Scene contains agent, goal, and obstacles. 
* Goal: The agent must navigate the grid to the goal while avoiding the obstacles.
* Agents: The environment contains one agent linked to a single brain.
* Agent Reward Function: 
    * -0.01 for every step.
    * +1.0 if the agent navigates to the goal position of the grid (episode ends).
    * -1.0 if the agent navigates to an obstacle (episode ends).
* Brains: One brain with the following observation/action space.
    * Vector Observation space: None
    * Vector Action space: (Discrete) Size of 4, corresponding to movement in cardinal directions.
    * Visual Observations: One corresponding to top-down view of GridWorld.
* Reset Parameters: Three, corresponding to grid size, number of obstacles, and number of goals.
* Benchmark Mean Reward: 0.8

## [Tennis](https://youtu.be/RDaIh7JX6RI)

![Tennis](images/tennis.png)

* Set-up: Two-player game where agents control rackets to bounce ball over a net. 
* Goal: The agents must bounce ball between one another while not dropping or sending ball out of bounds.
* Agents: The environment contains two agent linked to a single brain named TennisBrain. After training you can attach another brain named MyBrain to one of the agent to play against your trained model. 
* Agent Reward Function (independent): 
    * +0.1 To agent when hitting ball over net.
    * -0.1 To agent who let ball hit their ground, or hit ball out of bounds.
* Brains: One brain with the following observation/action space.
    * Vector Observation space: (Continuous) 8 variables corresponding to position and velocity of ball and racket.
    * Vector Action space: (Continuous) Size of 2, corresponding to movement toward net or away from net, and jumping.
    * Visual Observations: None.
* Reset Parameters: One, corresponding to size of ball.
* Benchmark Mean Reward: 2.5
* Optional Imitation Learning scene: `TennisIL`.

## [Push Block](https://youtu.be/jKdw216ZgoE)

![Push](images/push.png)

* Set-up: A platforming environment where the agent can push a block around.
* Goal: The agent must push the block to the goal.
* Agents: The environment contains one agent linked to a single brain.
* Agent Reward Function: 
    * -0.0025 for every step.
    * +1.0 if the block touches the goal.
* Brains: One brain with the following observation/action space.
    * Vector Observation space: (Continuous) 15 variables corresponding to position and velocities of agent, block, and goal.
    * Vector Action space: (Continuous) Size of 2, corresponding to movement in X and Z directions.
    * Visual Observations (Optional): One first-person camera. Use `VisualPushBlock` scene.
* Reset Parameters: None.
* Benchmark Mean Reward: 4.5
* Optional Imitation Learning scene: `PushBlockIL`.

## [Wall Jump](https://youtu.be/NITLug2DIWQ)

![Wall](images/wall.png)

* Set-up: A platforming environment where the agent can jump over a wall.
* Goal: The agent must use the block to scale the wall and reach the goal.
* Agents: The environment contains one agent linked to two different brains. The brain the agent is linked to changes depending on the height of the wall.
* Agent Reward Function: 
    * -0.0005 for every step.
    * +1.0 if the agent touches the goal.
    * -1.0 if the agent falls off the platform.
* Brains: Two brains, each with the following observation/action space.
    * Vector Observation space: (Continuous) 16 variables corresponding to position and velocities of agent, block, and goal, plus the height of the wall.
    * Vector Action space: (Discrete) Size of 74, corresponding to 14 raycasts each detecting 4 possible objects. plus the global position of the agent and whether or not the agent is grounded.
    * Visual Observations: None.
* Reset Parameters: 4, corresponding to the height of the possible walls.
* Benchmark Mean Reward (Big & Small Wall Brain): 0.8

## [Reacher](https://youtu.be/2N9EoF6pQyE)

![Reacher](images/reacher.png)

* Set-up: Double-jointed arm which can move to target locations.
* Goal: The agents must move it's hand to the goal location, and keep it there.
* Agents: The environment contains 10 agent linked to a single brain.
* 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 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.
* Benchmark Mean Reward: 30

## [Crawler](https://youtu.be/ftLliaeooYI)

![Crawler](images/crawler.png)

* Set-up: A creature with 4 arms and 4 forearms.
* Goal: The agents must move its body toward the goal direction without falling.
    * `CrawlerStaticTarget` - Goal direction is always forward.
    * `CrawlerDynamicTarget`- Goal direction is randomized.
* Agents: The environment contains 3 agent linked to a single brain.
* Agent Reward Function (independent): 
    * +0.03 times body velocity in the goal direction.
    * +0.01 times body direction alignment with goal direction.
* Brains: One brain with the following observation/action space.
    * Vector Observation space: (Continuous) 117 variables corresponding to position, rotation, velocity, and angular velocities of each limb plus the acceleration and angular acceleration of the body.
    * Vector Action space: (Continuous) Size of 20, corresponding to target rotations for joints. 
    * Visual Observations: None.
* Reset Parameters: None
* Benchmark Mean Reward: 2000

## [Banana Collector](https://youtu.be/heVMs3t9qSk)

![Banana](images/banana.png)

* Set-up: A multi-agent environment where agents compete to collect bananas. 
* Goal: The agents must learn to move to as many yellow bananas as possible while avoiding blue bananas.
* Agents: The environment contains 5 agents linked to a single brain.
* Agent Reward Function (independent): 
    * +1 for interaction with yellow banana
    * -1 for interaction with blue banana.
* Brains: One brain with the following observation/action space.
    * Vector Observation space: (Continuous) 53 corresponding to velocity of agent (2), whether agent is frozen and/or shot its laser (2), plus ray-based perception of objects around agent's forward direction (49; 7 raycast angles with 7 measurements for each).
    * Vector Action space: (Continuous) Size of 3, corresponding to forward movement, y-axis rotation, and whether to use laser to disable other agents.
    * Visual Observations (Optional): First-person camera per-agent. Use `VisualBanana` scene. 
* Reset Parameters: None.
* Benchmark Mean Reward: 10
* Optional Imitation Learning scene: `BananaIL`. 

## [Hallway](https://youtu.be/53GyfpPQRUQ)

![Hallway](images/hallway.png)

* 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):
    * +1 For moving to correct goal.
    * -0.1 For moving to incorrect goal.
    * -0.0003 Existential penalty.
* Brains: One brain with the following observation/action space:
    * Vector Observation space: (Continuous) 30 corresponding to local ray-casts detecting objects, goals, and walls.
    * Vector Action space: (Discrete) 4 corresponding to agent rotation and forward/backward movement.
    * Visual Observations (Optional): First-person view for the agent. Use `VisualHallway` scene.
* Reset Parameters: None.
* Benchmark Mean Reward: 0.7
* Optional Imitation Learning scene: `HallwayIL`.

## [Bouncer](https://youtu.be/Tkv-c-b1b2I)

![Bouncer](images/bouncer.png)

* Set-up: Environment where the agent needs on-demand decision making. The agent must decide how perform its next bounce only when it touches the ground.
* Goal: Catch the floating banana. Only has a limited number of jumps.
* Agents: The environment contains one agent linked to a single brain.
* Agent Reward Function (independent):
    * +1 For catching the banana.
    * -1 For bouncing out of bounds.
    * -0.05 Times the action squared. Energy expenditure penalty.
* Brains: One brain with the following observation/action space:
    * Vector Observation space: (Continuous) 6 corresponding to local position of agent and banana.
    * Vector Action space: (Continuous) 3 corresponding to agent force applied for the jump.
    * Visual Observations: None.
* Reset Parameters: None.
* Benchmark Mean Reward: 2.5

## [Soccer Twos](https://youtu.be/Hg3nmYD3DjQ)

![SoccerTwos](images/soccer.png)

* Set-up: Environment where four agents compete in a 2 vs 2 toy soccer game. 
* Goal:
    * Striker: Get the ball into the opponent's goal.
    * Goalie: Prevent the ball from entering its own goal.
* Agents: The environment contains four agents, with two linked to one brain (strikers) and two linked to another (goalies).
* Agent Reward Function (dependent):
    * Striker:
        * +1 When ball enters opponent's goal.
        * -0.1 When ball enters own team's goal.
        * -0.001 Existential penalty.
    * Goalie:
        * -1 When ball enters team's goal.
        * +0.1 When ball enters opponents goal.
        * +0.001 Existential bonus.
* Brains: Two brain with the following observation/action space:
    * Vector Observation space: (Continuous) 112 corresponding to local 14 ray casts, each detecting 7 possible object types, along with the object's distance. Perception is in 180 degree view from front of agent.
    * Vector Action space: (Discrete) 
        * Striker: 6 corresponding to forward, backward, sideways movement, as well as rotation.
        * Goalie: 4 corresponding to forward, backward, sideways movement.
    * Visual Observations: None.
* Reset Parameters: None
* Benchmark Mean Reward (Striker & Goalie Brain): 0 (the means will be inverse of each other and criss crosses during training)

## Walker

![Walker](images/walker.png)

* Set-up: Physics-based Humanoids agents with 26 degrees of freedom. These DOFs correspond to articulation of the following body-parts: hips, chest, spine, head, thighs, shins, feets, arms, forearms and hands. 
* Goal: The agents must move its body toward the goal direction as quickly as possible without falling.
* Agents: The environment contains 11 independent agent linked to a single brain.
* Agent Reward Function (independent): 
    * +0.03 times body velocity in the goal direction.
    * +0.01 times head y position.
    * +0.01 times body direction alignment with goal direction.
    * -0.01 times head velocity difference from body velocity.
* Brains: One brain with the following observation/action space.
    * Vector Observation space: (Continuous) 215 variables corresponding to position, rotation, velocity, and angular velocities of each limb, along with goal direction.
    * Vector Action space: (Continuous) Size of 39, corresponding to target rotations applicable to the joints. 
    * Visual Observations: None.
* Reset Parameters: None.
* Benchmark Mean Reward: 1000

## Pyramids

![Pyramids](images/pyramids.png)

* Set-up: Environment where the agent needs to press a button to spawn a pyramid, then navigate to the pyramid, knock it over, and move to the gold brick at the top.
* Goal: Move to the golden brick on top of the spawned pyramid.
* Agents: The environment contains one agent linked to a single brain.
* Agent Reward Function (independent):
    * +2 For moving to golden brick (minus 0.001 per step).
* Brains: One brain with the following observation/action space:
    * Vector Observation space: (Continuous) 148 corresponding to local ray-casts detecting switch, bricks, golden brick, and walls, plus variable indicating switch state.
    * Vector Action space: (Discrete) 4 corresponding to agent rotation and forward/backward movement.
    * Visual Observations (Optional): First-person camera per-agent. Use `VisualPyramids` scene. 
* Reset Parameters: None.
* Optional Imitation Learning scene: `PyramidsIL`.
* Benchmark Mean Reward: 1.75