howto.rl.att.003.rst

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

.. automodule:: mlpro.rl.examples.howto_rl_att_003_train_and_reload_single_agent_mujoco_sd_cartpole_continuous

Prerequisites

Please install the following packages to run this examples properly:

• OpenAI Gym
• Stable-Baselines3

Executable code

.. literalinclude:: ../../../../../../../../src/mlpro/rl/examples/howto_rl_att_003_train_and_reload_single_agent_mujoco_sd_cartpole_continuous.py
        :language: python

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:

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

• :ref:`MLPro-RL: Training <target_training_RL>`
• :ref:`Howto RL-AGENT-022: Train and Reload Single Agent (MuJoCo) <Howto Agent RL 022>`
• :ref:`API Reference <target_api_rl_run_train>`