Initial Commit

.gitignore

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+# 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/
+pip-wheel-metadata/
+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
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.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
+
+# celery beat schedule file
+celerybeat-schedule
+
+# 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/
+
+# Misc
+models/

event_logger.py

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+import logging
+
+from tensorboardX import SummaryWriter
+
+
+class EventLogger:
+    def __init__(self, root_dir):
+        self.root_dir = root_dir
+        if root_dir is None:
+            self.tensorboard_logger = None
+        else:
+            root_dir.mkdir(parents=True, exist_ok=False)
+            self.tensorboard_logger = SummaryWriter(str(root_dir))
+        self.console = logging.getLogger(__name__)
+
+    def log_scalar(self, tag, value, iteration):
+        if self.tensorboard_logger is not None:
+            self.tensorboard_logger.add_scalar(tag, value, iteration)
+
+    def debug(self, msg):
+        self.console.debug(msg)
+
+    def info(self, msg):
+        self.console.info(msg)
+
+    def warning(self, msg):
+        self.console.warning(msg)
+
+    def error(self, msg):
+        self.console.error(msg)
+
+    def critical(self, msg):
+        self.console.critical(msg)

model.py

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+import numpy as np
+import torch
+import torch.nn as nn
+from torch.distributions import Categorical
+
+
+EPS = np.finfo(np.float32).eps.item()
+
+
+class Policy(nn.Module):
+    def __init__(self, num_features, num_actions):
+        super().__init__()
+
+        self.num_features = num_features
+        self.num_actions = num_actions
+
+        layer_sizes = [126, 64]
+        dropout_probs = [0.5, 0.75]
+        self.network = nn.Sequential(
+            nn.Linear(num_features, layer_sizes[0]),
+            nn.ReLU(),
+            nn.Dropout(dropout_probs[0]),
+            nn.Linear(layer_sizes[0], layer_sizes[1]),
+            nn.ReLU(),
+            nn.Dropout(dropout_probs[1]),
+            nn.Linear(layer_sizes[1], num_actions),
+            nn.Softmax(dim=-1)
+        )
+
+    def _expand_mask(self, mask):
+        expanded_mask = [0 for x in range(self.num_actions)]
+        for i in mask:
+            expanded_mask[i] = 1
+        return expanded_mask
+
+    def predict(self, state, mask):
+        action_probs = self.network(torch.FloatTensor(state))
+        mask = torch.FloatTensor(self._expand_mask(mask))
+        masked_probs = action_probs * mask
+        # Guard against all-zero probabilities
+        guard_probs = torch.full((self.num_actions,), EPS) * mask
+        return masked_probs + guard_probs
+
+    def predict_masked_normalized(self, state, mask):
+        action_probs = self.network(torch.FloatTensor(state))
+        mask = torch.ByteTensor(self._expand_mask(mask))
+        masked_probs = torch.masked_select(action_probs, mask)
+        # Guard against all-zero probabilities
+        masked_probs += torch.full((len(masked_probs),), EPS)
+        normalized_probs = masked_probs / masked_probs.sum()
+        return normalized_probs
+
+    def sample_action(self, state, mask):
+        probs = self.predict(state, mask)
+        distribution = Categorical(probs)
+        action = distribution.sample()
+        return action.item()
+
+    def sample_action_with_log_probability(self, state, mask):
+        probs = self.predict(state, mask)
+        distribution = Categorical(probs)
+        action = distribution.sample()
+        log_prob = distribution.log_prob(action)
+        return action, log_prob
+
+    @staticmethod
+    def save(model, path):
+        model_descriptor = {
+            'num_features': model.num_features,
+            'num_actions': model.num_actions,
+            'network': model.state_dict()
+        }
+        torch.save(model_descriptor, path)
+
+    @staticmethod
+    def load(path):
+        model_descriptor = torch.load(path)
+        num_features = model_descriptor['num_features']
+        num_actions = model_descriptor['num_actions']
+        model = Policy(num_features, num_actions)
+        model.load_state_dict(model_descriptor['network'])
+        return model
+
+    @staticmethod
+    def load_for_eval(path):
+        model = Policy.load(path)
+        model.eval()
+        return model