Gymnasium<-WERDNA RL GYM->PPO

• Fix observation space

Scripts

Run Training Inference

train.py 
train_advanced.py #recommended

To Run Test Model

test_model.py
test_model_advanced.py #recommended

To Run a Simple Teleoperation from Trained Agent

model_teleop.py
model_teleop_advanced.py #recommended

Tensorboard Viewing

To run tensorboard, simply run:

tensoboard --logdir logs/xxx

Adding New Environment

Can simply add one more environment under env directory. Once added, make sure to update the train.py or train_advanced.py script to include ur environment in. To run training script, add one more <custom_configuration>.yaml under config section.

Configuration For Advanced

Configuration Set up is to specify:

• Robot Model: path to the URDF file
• Environment: custom environment name
• Connect_Type: Whether to train on CPU or GPU
• Biases: Types of rewards biases to priotize during training
• ec Since the agents are trained using PPO, the entrophy coefficient is a method to stablize and encourage exploration during training to avoid either overfitting/underfitting or overtrained/undertrained conditions
• Filename: The name of the trained agent's file
• Timesteps: Number of timesteps per training session
• Record Video: Whether to record video in the kernel_pca_evaluation script, recorded video will be saved to video directory.

robot_model: "models/werdna_revised_bullet.urdf"
environment: "werdna_advanced"
connect_type: "DIRECT"
device: "cpu"

biases:
  r_bias: 0.0
  p_bias: 0.3
  y_bias: 0.25
  dR_bias: 0.0
  dP_bias: 0.2
  dY_bias: 0.0
  x_bias: 0.25
  v_bias: 0.0

ec: 0.01

filename: "werdna_advanced_v2"

timesteps: 500000
record_video: false

The results or what you would called trained agent is saved in the results directory, but under a new directory that is named after the environment's name and biases specified