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mlpro.rl.examples.howto_rl_015_train_wrapped_sb3_policy_with_stagnation_detection

Prerequisites

Please install the following packages to run this examples properly:

Executable code

../../../../../src/mlpro/rl/examples/howto_rl_015_train_wrapped_sb3_policy_with_stagnation_detection.py

Results

image

After the environment is initialised, the training will run for the specified amount of limits. When stagnation is detected, the training will be stopped.

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          : HH:MM:SS.SSSSSS 
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      :  
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Training cycles   :  
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Evaluation cycles :  
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Adaptations       :  
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- High score        :  
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 : 120 
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: -- Evaluations       : 25 
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: ------------------------------------------------------------------------------ 
YYYY-MM-DD  HH:MM:SS.SSSSSS  W  Results  RL: ------------------------------------------------------------------------------
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