test

.gitignore

@@ -0,0 +1,162 @@
+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+

PPO.py

@@ -239,7 +239,7 @@ class Arguments:
         self.num_threads = 32  # cpu_num for evaluate model, torch.set_num_threads(self.num_threads)
 
         '''Arguments for training'''
-        self.num_episode = 1000  # to control the train episodes for PPO
+        self.num_episode = 2000  # to control the train episodes for PPO
         self.gamma = 0.995  # discount factor of future rewards
         self.learning_rate = 2 ** -14  # 2e-4
         self.soft_update_tau = 2 ** -8  # 2 ** -8 ~= 5e-3
@@ -365,11 +365,11 @@ if __name__ == '__main__':
         print('actor parameters have been saved')
 
     if args.test_network:
-        args.cwd = agent_name + '0618'
+        args.cwd = agent_name
         agent.act.load_state_dict(torch.load(act_save_path))
         print('parameters have been reload and test')
         record = test_one_episode(env, agent.act, agent.device)
-        eval_data = pd.DataFrame(record['information'])
+        eval_data = pd.DataFrame(record['system_info'])
         eval_data.columns = ['time_step', 'price', 'netload', 'action', 'real_action', 'soc', 'battery', 'gen1', 'gen2',
                              'gen3', 'unbalance', 'operation_cost']
     if args.save_test_data:

SAC.py

@@ -107,7 +107,7 @@ if __name__ == '__main__':
         agent.act.load_state_dict(torch.load(act_save_path))
         print('parameters have been reload and test')
         record = test_one_episode(env, agent.act, agent.device)
-        eval_data = pd.DataFrame(record['information'])
+        eval_data = pd.DataFrame(record['system_info'])
         eval_data.columns = ['time_step', 'price', 'netload', 'action', 'real_action', 'soc', 'battery', 'gen1', 'gen2',
                              'gen3', 'unbalance', 'operation_cost']
     if args.save_test_data:

TD3.py

@@ -103,7 +103,7 @@ if __name__ == '__main__':
         agent.act.load_state_dict(torch.load(act_save_path))
         print('parameters have been reload and test')
         record = test_one_episode(env, agent.act, agent.device)
-        eval_data = pd.DataFrame(record['information'])
+        eval_data = pd.DataFrame(record['system_info'])
         eval_data.columns = ['time_step', 'price', 'netload', 'action', 'real_action', 'soc', 'battery', 'gen1', 'gen2',
                              'gen3', 'unbalance', 'operation_cost']
     if args.save_test_data:

tools.py

@@ -131,7 +131,7 @@ class Arguments:
         self.num_threads = 32  # cpu_num for evaluate model, torch.set_num_threads(self.num_threads)
 
         '''Arguments for training'''
-        self.num_episode = 1000
+        self.num_episode = 2000
         self.gamma = 0.995  # discount factor of future rewards
         # self.reward_scale = 1  # an approximate target reward usually be closed to 256
         self.learning_rate = 2 ** -14  # 2 ** -14 ~= 6e-5