howto.rl.agent.011.rst

Howto RL-AGENT-011: Train and Reload Single Agent (Gym)

.. automodule:: mlpro.rl.examples.howto_rl_agent_011_train_and_reload_single_agent_gym

Prerequisites

Please install the following packages to run this examples properly:

• OpenAI Gym
• Stable-Baselines3

Executable code

.. literalinclude:: ../../../../../../../../src/mlpro/rl/examples/howto_rl_agent_011_train_and_reload_single_agent_gym.py
        :language: python

Results

The Gym Cartpole environment window appears. Afterwards, the training runs for a few episodes before terminating and printing the result.

After termination the local result folders contain the training result files:

• agent_actions.csv
• env_rewards.csv
• env_states.csv
• evaluation.csv
• summary.csv
• trained model.pkl

Both training results are from the same agent.

Cross Reference

• :ref:`MLPro-RL: Training <target_training_RL>`
• :ref:`API Reference <target_api_rl_run_train>`