howto.rl.mb.001.rst

Howto RL-MB-001: Train and Reload Model Based Agent (Gym)

.. automodule:: mlpro.rl.examples.howto_rl_mb_001_train_and_reload_model_based_agent_gym

Prerequisites

Please install the following packages to run this examples properly:

• PyTorch
• OpenAI Gym

Executable code

.. literalinclude:: ../../../../../../../../src/mlpro/rl/examples/howto_rl_mb_001_train_and_reload_model_based_agent_gym.py
        :language: python

Results

After the environment is initiated, the training will run for the specified amount of limits. The expected initial console output can be seen below.

YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Training "RL": ------------------------------------------------------------------------------
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Training "RL": -- Training episode 0 started...
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Training "RL": ------------------------------------------------------------------------------

YYYY-MM-DD  HH:MM:SS.SSSSSS  I  Agent "": Instantiated
YYYY-MM-DD  HH:MM:SS.SSSSSS  S  Agent "": Adaptivity switched on
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Training "RL": ------------------------------------------------------------------------------
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Training "RL": ------------------------------------------------------------------------------
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Training "RL": -- Training run 0 started...
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Training "RL": ------------------------------------------------------------------------------
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Training "RL": ------------------------------------------------------------------------------

YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Training "RL": ------------------------------------------------------------------------------
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Training "RL": -- Training episode 0 started...
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Training "RL": ------------------------------------------------------------------------------

YYYY-MM-DD  HH:MM:SS.SSSSSS  I  Agent "": Action computation started
YYYY-MM-DD  HH:MM:SS.SSSSSS  I  Agent "": Action computation finished
YYYY-MM-DD  HH:MM:SS.SSSSSS  S  Agent "": Adaptation started
YYYY-MM-DD  HH:MM:SS.SSSSSS  I  Agent "": Action computation started
...

After termination the local result folder contains the training result files:

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

Cross Reference

• :ref:`API Reference - RL Agent <target_api_rl_agents>`
• :ref:`API Reference - RL Environments <target_api_rl_env>`
• :ref:`API Reference - Environment Model <target_api_rl_env_ada>`
• :ref:`API Reference - RL Scenario and Training <target_api_rl_run_train>`