feat: initial commit,

.gitignore

@@ -0,0 +1,162 @@
+# 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/
+
+vid

.pylintrc

@@ -0,0 +1,3 @@
+[DESIGN]
+# Maximum number of statements in function / method body
+max-statements=16

main.py

@@ -1,28 +1,145 @@
+"""
+Code used to solve MountainCar-v0 gymnasium problem using Q-Learning algorithm
+"""
+from datetime import datetime
 import gymnasium as gym
+import numpy as np
 
-if __name__ == "__main__":
-    # init env
-    env = gym.make("MountainCar-v0", render_mode="rgb_array")
 
-    # wrapper to record the video at 3rd episode and saves it to the folder
+def initialize_environment():
-    # 'vid'
+    """
+    Initialize environment and video recording
+    """
+    # Initialize environment
+    env = gym.make('MountainCar-v0', render_mode='rgb_array')
+    # Save video
+    now = datetime.now()
+    time_string = now.strftime("%H:%M:%S")
     env = gym.wrappers.RecordVideo(
-        env, video_folder="vid", episode_trigger=lambda x: x == 3
+        env,
-    )
+        video_folder='vid',
+        episode_trigger=lambda x: x == 1,
+        disable_logger=False,
+        name_prefix=time_string)
+    return env
 
-    # an episode ends if goal is reached or other game ending factors (e.g.
-    # reached max steps)
-    n_episodes = 4
-    for episode in range(n_episodes):  # iterate episodes
-        state, info = env.reset()  # reset the env to an initial state
-        done = False  # boolean to stop an episode
 
-        while not done:  # iterate steps
+def initialize_q_table(env):
-            # randomly choose a sample
+    """
-            action = env.action_space.sample()
+    Initialize "empty" Q-table
-            # take the action (step) and observe the state and reward
+    """
-            next_state, reward, terminated, truncated, info = env.step(action)
+    # Initialize Q-table
-            # condition to stop an episode
+    n_actions = env.action_space.n  # Number of possible actions, should be 3
-            done = terminated or truncated
+    # 0 accelerate left
+    # 1 dont accelerate
+    # 2 accelerate to the right
+    q_table = np.zeros((n_actions,))
+    return q_table
 
-    env.close()
+
+def initialize_hyperparameters():
+    """
+    Initialize hyperparameters used by algorithm
+    """
+    hyperparameters = {
+        "learning_rate": 0.1,
+        "discount_factor": 0.99,
+        "epsilon": 0.2,
+        "max_episodes": 1
+    }
+    return hyperparameters
+
+
+def choose_action(hyperparameters, env, q_table):
+    """
+    Choose one of 3 actions possible for the algorithm
+    """
+    # hyperparameters["epsilon"]-greedy exploration-exploitation tradeoff
+    if np.random.uniform(0, 1) < hyperparameters["epsilon"]:
+        action = env.action_space.sample()  # Choose a random action
+    else:
+        # Choose the action with the highest Q-value
+        action = np.argmax(q_table)
+    return action
+
+
+def update_q_table(q_table, action, hyperparameters, reward):
+    """
+    Update q_table with newest reward
+    """
+    # Q-table update
+    q_value = q_table[action]
+    max_q_value = np.max(q_table)
+    new_q_value = (1 - hyperparameters["learning_rate"]) * q_value + \
+        hyperparameters["learning_rate"] * \
+        (reward + hyperparameters["discount_factor"] * max_q_value)
+    q_table[action] = new_q_value
+    return q_table
+
+
+def movement(hyperparameters, env, q_table, total_reward=0):
+    """
+    Choose action and observe consequences
+    """
+    action = choose_action(hyperparameters, env, q_table)
+    # Take the action and observe the next state
+    next_state, reward, terminated, truncated, info = env.step(action)
+    done = terminated or truncated
+    q_table = update_q_table(q_table, action, hyperparameters, reward)
+
+    total_reward += reward
+    return hyperparameters, env, q_table, done, total_reward
+
+
+def episode_step(env, hyperparameters, q_table, episode_rewards):
+    """
+    Actions done with every episode
+    """
+    state, _ = env.reset()  # Reset the environment to an initial state
+    done = False  # Boolean to indicate episode completion
+    total_reward = 0  # Accumulate rewards for the episode
+
+    while not done:
+        hyperparameters, env, q_table, done, total_reward = movement(
+            hyperparameters, env, q_table, total_reward)
+
+    episode_rewards.append(total_reward)
+    return env, hyperparameters, q_table, episode_rewards
+
+
+def training_loop(hyperparameters, env, q_table):
+    """
+    Actual training for MountainCar
+    """
+    episode_rewards = []  # List to store episode rewards
+
+    for episode in range(hyperparameters["max_episodes"]):
+        env, hyperparameters, q_table, episode_rewards = episode_step(
+            env, hyperparameters, q_table, episode_rewards)
+
+    return env, q_table
+
+
+def inference(env, q_table):
+    """
+    Inference using the updated Q-table
+    """
+    state, _ = env.reset()
+    done = False
+
+    while not done:
+        # Choose the action with the highest Q-value
+        action = np.argmax(q_table)
+        # Take the action and observe the next state
+        next_state, reward, terminated, truncated, info = env.step(action)
+        done = terminated or truncated
+
+
+if __name__ == '__main__':
+    ENV = initialize_environment()
+    Q_TABLE = initialize_q_table(ENV)
+    HYPERPARAMETERS = initialize_hyperparameters()
+    ENV, Q_TABLE = training_loop(HYPERPARAMETERS, ENV, Q_TABLE)
+    inference(ENV, Q_TABLE)
+
+    ENV.close()

requirements.yml

@@ -9,3 +9,4 @@ dependencies:
   - numpy
   - python=3.9
   - pygame
+  - opencv-python