Orbit is a open-source project and a collection of reinforcement learning environments. If you want to contribute to this project by creating your own environment then please drop a mail to [email protected]. You can know how to create an environment through this blog:

• python - 3.7
• keras - 2.4.3
• tensorflow - 2.2.0

Checkout random_policy.py to see how to use an environment.

Orbit contains following RL environments as of today.

The task is to take the ball on paddle. Inbuilt turtle library is used to create the environment.

Action space (3)

• 0 - move paddle to left
• 1 - do nothing
• 2 - move paddle to right

State space (5)

• x position of paddle
• x and y position of ball
• x and y velocity of ball

Reward function

• +3.0 - when paddle hit the ball
• -3.0 - when ball touchs the ground
• -0.1 - when paddle moves

Episode termination

• Episode ends when ball touchs the ground.

The task to dodge the kangaroo from moving balls. Inbuilt turtle library is used to create the environment.

Action space (2)

• 0 - jump
• 1 - do nothing

State space (13)

• y position of kangaroo
• x and y position of all moving ball

Reward function

• +5.0 - when kangaroo dodge a ball
• -1.0 - for each jump
• +0.1 - for each time step

Episode termination

• Episode ends when kangaroo touchs a ball.

The task to hit the dropping balls. pygame library is used to create the environment.

Action space (4)

• 0 - rotate left
• 1 - rotate right
• 2 - shoot bullet
• 3 - do nothing

State space (8)

• x position of cannon
• x and y position of all balls
• sin of angle of cannon

Reward function

• +5.0 - hitting the ball
• -0.5 - shooting bullet
• -0.1 - rotating canon
• -5.0 - episode termination

Episode termination

• Episode ends when a ball touchs the line.