shivaverma / Orbit
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Orbit
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:
Requirements
-
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.
Environment 1: Paddle
Shiva Verma
Author: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.
Environment 2: Jump
Vinod Kumar
Author: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.
Environment 3: Cannon
Sathish Kumar
Author: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.