Howto RL-ATT-002: Train and Reload Single Agent using Stagnation Detection Cartpole Discrete (MuJoCo)

Prerequisites

Please install the following packages to run this examples properly:

Executable code

## -------------------------------------------------------------------------------------------------
## -- Project : MLPro - A Synoptic Framework for Standardized Machine Learning Tasks
## -- Package : mlpro.rl.examples
## -- Module  : howto_rl_att_002_train_and_reload_single_agent_mujoco_sd_cartpole_discrete.py
## -------------------------------------------------------------------------------------------------
## -- History :
## -- yyyy-mm-dd  Ver.      Auth.    Description
## -- 2023-03-04  1.0.0     DA       Creation as derivate of howto_rl_agent_021 
## -- 2023-03-07  1.0.1     MRD      Renamed
## -- 2023-03-27  1.0.2     DA       Refactoring
## -------------------------------------------------------------------------------------------------

"""
Ver. 1.0.2 (2023-03-27)

As in Howto RL AGENT 021, this module shows how to train a single agent with SB3 Policy on Discrete 
Cartpole MuJoCo Environment. In opposite to howto 021, stagnation detection is used to automatically 
end the training if no further progress can be made.

You will learn:

1. How to use MLPro's RLScenario class.

2. How to create sb3 policy object.

3. How to create SB3 policy in MLPro.

4. How to setup and run RLTraining in MLPro.

"""


from stable_baselines3 import PPO
from mlpro.rl import *
from mlpro.wrappers.sb3 import WrPolicySB32MLPro
from mlpro.rl.pool.envs.cartpole import CartpoleMujocoDiscrete
from pathlib import Path


class MyScenario(RLScenario):
    C_NAME = "Matrix"

    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging) -> Model:
        # 1.1 Setup environment
        self._env = CartpoleMujocoDiscrete(p_logging=logging, p_visualize=visualize)

        # 1.2 Setup Policy From SB3
        policy_sb3 = PPO(policy="MlpPolicy", n_steps=10, env=None, _init_setup_model=False, device="cpu", seed=1)

        # 1.3 Wrap the policy
        policy_wrapped = WrPolicySB32MLPro(
            p_sb3_policy=policy_sb3,
            p_cycle_limit=self._cycle_limit,
            p_observation_space=self._env.get_state_space(),
            p_action_space=self._env.get_action_space(),
            p_ada=p_ada,
            p_visualize=p_visualize,
            p_logging=p_logging
        )

        # 1.4 Setup standard single-agent with own policy
        return Agent(
            p_policy=policy_wrapped, p_envmodel=None, p_name="Smith", p_ada=p_ada, p_visualize=p_visualize, p_logging=p_logging
        )


# 3 Create scenario and run some cycles
if __name__ == "__main__":
    # Parameters for demo mode
    cycle_limit = 20000
    adaptation_limit = 0
    stagnation_limit = 5
    eval_frequency = 10
    eval_grp_size = 5
    logging = Log.C_LOG_WE
    visualize = True
    path = str(Path.home())

else:
    # Parameters for internal unit test
    cycle_limit = 50
    adaptation_limit = 5
    stagnation_limit = 5
    eval_frequency = 2
    eval_grp_size = 1
    logging = Log.C_LOG_NOTHING
    visualize = False
    path = str(Path.home())


# 2 Create scenario and start training
training = RLTraining(
    p_scenario_cls=MyScenario,
    p_cycle_limit=cycle_limit,
    p_adaptation_limit=adaptation_limit,
    p_stagnation_limit=stagnation_limit,
    p_eval_frequency=eval_frequency,
    p_eval_grp_size=eval_grp_size,
    p_path=path,
    p_visualize=visualize,
    p_logging=logging,
)


# 3 Training
training.run()
filename_scenario = training.get_scenario().get_filename()


# 4 Reload the scenario
if __name__ == '__main__':
    input( '\nTraining finished. Press ENTER to reload and run the scenario...\n')

scenario = MyScenario.load( p_path = training.get_training_path() + os.sep + 'scenario',
                            p_filename = filename_scenario )


# 5 Reset Scenario
scenario.reset()  


# 6 Run Scenario
scenario.run()

if __name__ != '__main__':
    from shutil import rmtree
    rmtree(training.get_training_path())
else:
    input( '\nPress ENTER to finish...')

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:

../../../../../../_images/howto_rl_att_002_evaluation.png
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