ml-agents/docs/Learning-Environment-Examples.md

Example Learning Environments

The Unity ML-Agents toolkit contains an expanding set of example environments which demonstrate various features of the platform. Environments are located in MLAgentsSDK/Assets/ML-Agents/Examples and summarized below. Additionally, our first ML Challenge 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 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 page.

Basic

• 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: 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

• 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: 8 variables corresponding to rotation of platform, and position, rotation, and velocity of ball.
  • Vector Observation space (Hard Version): 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

• 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

• 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: 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

• 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) 70 variables corresponding to 14 ray-casts each detecting one of three possible objects (wall, goal, or block).
  • 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

• 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: 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.
  • Vector Action space: (Discrete) 4 Branches:
    • Forward Motion (3 possible actions: Forward, Backwards, No Action)
    • Rotation (3 possible acions: Rotate Left, Rotate Right, No Action)
    • Side Motion (3 possible acions: Left, Right, No Action)
    • Jump (2 possible actions: Jump, No Action)
  • Visual Observations: None.
• Reset Parameters: 4, corresponding to the height of the possible walls.
• Benchmark Mean Reward (Big & Small Wall Brain): 0.8

Reacher

• 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: 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

• 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: 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

• 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: 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: (Discrete) 4 Branches:
    • Forward Motion (3 possible actions: Forward, Backwards, No Action)
    • Side Motion (3 possible acions: Left, Right, No Action)
    • Rotation (3 possible acions: Rotate Left, Rotate Right, No Action)
    • Laser (2 possible actions: Laser, No Action)
  • Visual Observations (Optional): First-person camera per-agent. Use VisualBanana scene.
• Reset Parameters: None.
• Benchmark Mean Reward: 10
• Optional Imitation Learning scene: BananaIL.

Hallway

• 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: 30 corresponding to local ray-casts detecting objects, goals, and walls.
  • Vector Action space: (Discrete) 1 Branch, 4 actions 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

• 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: 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

• 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: 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) One Branch
    • Striker: 6 actions corresponding to forward, backward, sideways movement, as well as rotation.
    • Goalie: 4 actions 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

• 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: 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

• 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: 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. Us VisualPyramids scene.
• Reset Parameters: None.
• Optional Imitation Learning scene: PyramidsIL.
• Benchmark Mean Reward: 1.75