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Howto RL-AGENT-002: Train an Agent with Own Policy

.. automodule:: mlpro.rl.examples.howto_rl_agent_002_train_agent_with_own_policy_on_gym_environment

Prerequisites

Please install the following packages to run this examples properly:

Executable code

.. literalinclude:: ../../../../../../../../src/mlpro/rl/examples/howto_rl_agent_002_train_agent_with_own_policy_on_gym_environment.py
        :language: python

Results

The Gym Cartpole environment window should appear. Afterwards, the training should run for a few episodes before terminating and printing the result. The training log is also stored in the location specified.

images/Cartpole.png

YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: ------------------------------------------------------------------------------
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Training Results of run 0
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: ------------------------------------------------------------------------------
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: ------------------------------------------------------------------------------
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Scenario          : RL-Scenario Matrix
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Model             : Agent Smith
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Start time stamp  : YYYY-MM-DD HH:MM:SS.SSSSSS
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- End time stamp    : YYYY-MM-DD HH:MM:SS.SSSSSS
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Duration          : 0:00:09.209252
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Start cycle id    : 0
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- End cycle id      : 499
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Training cycles   : 500
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Evaluation cycles : 0
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Adaptations       : 0
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- High score        : None
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Results stored in : "C:\Users\%username%\YYYY-MM-DD  HH:MM:SS Training RL"
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Training Episodes : 23
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Evaluations       : 0
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: ------------------------------------------------------------------------------
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: ------------------------------------------------------------------------------
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