Howto RL-ATT-003: Train and Reload Single Agent using Stagnation Detection Cartpole Continuous (MuJoCo)

Prerequisites

Please install the following packages to run this examples properly:

Executable code

Results

The MuJoCo Cartpole environment window appears during training and shows an improved control behavior after a while. After the training, the related scenario is reloaded and run for a further episode to demonstrate the final control behavior.

The training itself is terminated due to automatic stagnation detection. The chart below shows the training progress and the ending at the point of maximum possible reward:

../../../../../../_images/howto_rl_att_002_evaluation.png
After termination the local result folder contains the training result files:

  • agent_actions.csv

  • env_rewards.csv

  • env_states.csv

  • evaluation.csv

  • summary.csv

  • scenario

Cross Reference