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Training a 4 legged agent to walk in Crawler Unity Environment using Proximal Policy Optimization(PPO)algorithm
Crawler Unity Environment
- 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