Python code for implementation of the paper 'Reinforcement Learning-Based Adaptive PID Controller for DPS'.

Python libraries:

• numpy
• pandas
• tensorflow (version-2.3 is used in the paper)
• OrcFxAPI

Software:

• OrcaFlex developed by Orcina (version-10.3 is used in the paper) [NOTE: its license is required to use the OrcaFlex]

1. download the zip file or do git clone ... and save in your directory
2. pip install all the libraries for the dependency
3. check if the OrcaFlex recognizes Python (otherwise, read an OrcaFlex's documentation for Python API)
4. move the file DP/dp_force_source/dp_force_windff.cp37-win_amd64.pyd to the Lib directory where your python is installed

1. set self.TEST = False in DP/DP_DDPG/settings.py
2. open DP/DP_DDPG/DDPG_based_DPS.dat
3. run dynamic simulation
4. check the training results in DP/results/train/reward_func_case1
   *description for the results is documented in DP/results/README.md

1. set self.TEST = True in DP/DP_DDPG/settings.py
2. open DP/DP_DDPG/DDPG_based_DPS.dat
3. run dynamic simulation
4. check the testing results in DP/results/test/reward_func_case1

1. open DP/DP_ZN/ZN_based_DPS.dat ( ZN stands for Ziegler–Nichols )
2. run dynamic simulation
3. check the results in DP/results/ZN/temp

• environmental condition
  in DP/DDPG/DDPG_based_DPS.dat, you can change the environmental condition. In the same way, the environmental condition for the testing can be set.

• hyper-parameters for the DDPG
  the hyper-parameters are stated below '# params for ddpg' in DP/DP_DDPG/settings.py

• use of the fixed P, D base gains (= not to use the adaptive P, D gains)
  set self.use_max_PD_gains = True in DP/DP_DDPG/settings.py

• use of the fixed I base gains (= not to use the adaptive I gains)
  set self.use_max_I_gain = True in DP/DP_DDPG/settings.py

• use of the update-gates for the integral of the errors
  set self.use_gate_err_accumulation = True in DP/DP_DDPG/settings.py

• neural network (NN) architecture
  NN architecture with batch normalization: DP/baselines/ddpg/ddpg_simple_bn_only.py
  NN architecture without batch normalization: DP/baselines/ddpg/ddpg_simple.py
  *the above models can be switched by self.use_bn in DP/DP_DDPG/settings.py

• environmental condition
  in DP/DP_ZN/ZN_based_DPS.dat, you can change the environmental condition.

• fixed base gains
  in DP/DP_ZN/ZN_based_DPS.dat, there is 'Vessel1' under a 'Vessels' directory. Under a tag named 'Tags' in the 'Vessel1', you can access 'ExternalFunctionParameters' where you can see the fixed base PID gains. ( If you change the fixed base gains in DP/DP_ZN/ZN_based_DPS.dat, you should change the self.ZN_gains in DP/DP_DDPG/settings.py accordingly. )