Soccer Twos
In this projects we’ll implementing agents that learns to play Unity Soccer Twos using several Deep Rl algorithms. Unity Ml Agents is a toolkit for developing and comparing reinforcement learning algorithms. We’ll be using pytorch library for the implementation.
Libraries Used
- Unity Ml Agents
- PyTorch
- numpy
- matplotlib
About Enviroment
- Set-up: Environment where four agents compete in a 2 vs 2 toy soccer game.
- Goal:
- Get the ball into the opponent’s goal while preventing the ball from entering own goal.
- Goalie:
- Agents: The environment contains four agents, with the same Behavior Parameters : Soccer.
- Agent Reward Function (dependent):
- +1 When ball enters opponent’s goal.
- -1 When ball enters team’s goal.
- -0.001 Existential penalty.
- Behavior Parameters:
- Vector Observation space: 336 corresponding to 11 ray-casts forward distributed over 120 degrees (264) and 3 ray-casts backward distributed over 90 degrees each detecting 6 possible object types, along with the object’s distance. The forward ray-casts contribute 264 state dimensions and backward 72 state dimensions.
- Vector Action space: (Discrete) Three branched actions corresponding to forward, backward, sideways movement, as well as rotation.
- Visual Observations: None
- Float Properties: Two
- ball_scale: Specifies the scale of the ball in the 3 dimensions (equal across the three dimensions)
- Default: 7.5
- Recommended minimum: 4
- Recommended maximum: 10
- gravity: Magnitude of the gravity
- Default: 9.81
- Recommended minimum: 6
- Recommended maximum: 20
- ball_scale: Specifies the scale of the ball in the 3 dimensions (equal across the three dimensions)
Deep RL Agents
Any questions
If you have any questions, feel free to ask me:
- Mail: deepanshut041@gmail.com
- Github: https://github.com/deepanshut041/Reinforcement-Learning
- Website: https://deepanshut041.github.io/Reinforcement-Learning
- Twitter: @deepanshut041
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