Add files via upload

Cagi/QLAgent.py

@@ -0,0 +1,376 @@
+import gym
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+import numpy as np
+
+def run_q_learning(agent, env, _):
+    pass
+
+def initialize_q_table(num_states, num_actions):
+    return np.zeros((num_states, num_actions))
+
+# Example usage:
+num_states = 4  # Number of states
+num_actions = 2  # Number of actions
+Q = initialize_q_table(num_states, num_actions)
+
+def num_actions(env):
+    return env.action_space.n
+
+def update_q_table(self, state, action, reward, next_state):
+    pass
+
+def q_table(env):
+    # Assuming env is a Gym environment
+    if isinstance(env.observation_space, gym.spaces.Discrete) and isinstance(env.action_space, gym.spaces.Discrete):
+        return np.zeros((env.observation_space.n, env.action_space.n))
+    else:
+        raise ValueError("The environment's state and action space should be discrete for Q-table approach.")
+
+def q_learning(env, q_table, learning_rate=0.1, discount_factor=0.9, exploration_prob=0.1, episodes=1000):
+    for episode in range(episodes):
+        state = env.reset()
+        done = False
+        while not done:
+            # Selecting action using epsilon-greedy strategy
+            if np.random.uniform(0, 1) < exploration_prob:
+                action = env.action_space.sample()
+            else:
+                action = np.argmax(q_table[state, :])
+
+            # Taking action and observing next state and reward
+            next_state, reward, done, _ = env.step(action)
+
+            # Updating Q-value
+            best_next_action = np.argmax(q_table[next_state, :])
+            td_target = reward + discount_factor * q_table[next_state, best_next_action]
+            td_error = td_target - q_table[state, action]
+            q_table[state, action] += learning_rate * td_error
+
+            state = next_state
+
+    return q_table
+
+def shape(space):
+    if isinstance(space, gym.spaces.Discrete):
+        return space.n
+    else:
+        return space.shape[0]
+
+def observation_space():
+    pass
+
+def action_space():
+    pass
+
+def QLearningAgent(self, q_table, observation_space, action_space, num_actions, learning_rate, discount_factor, exploration_prob, num_states, select_action):
+
+
+    def run_q_learning(agent, env, _):
+
+
+        def learning_rate():
+            pass
+
+        def discount_factor():
+            pass
+
+        def exploration_prob():
+            pass
+
+        def num_states():
+            pass
+
+        def env(observation_space, action_space, n):
+            pass
+
+def update_q_value(Q, state, action, reward, next_state, learning_rate, discount_factor):
+    if state < Q.shape[0] and action < Q.shape[1] and next_state < Q.shape[0]:
+        Q[state, action] += learning_rate * (reward + discount_factor * (np.max(Q[next_state, :]) - Q[state, action]))
+    else:
+        raise IndexError("Index out of bounds for Q-table")
+    return Q
+
+def accuracy_score(y_true, y_pred):
+    # Check if the lengths of y_true and y_pred match
+    if len(y_true) != len(y_pred):
+        raise ValueError("The lengths of y_true and y_pred should match")
+
+    # Count the number of correct predictions
+    correct_predictions = sum(1 for true, pred in zip(y_true, y_pred) if true == pred)
+
+    # Calculate the accuracy
+    accuracy = correct_predictions / len(y_true)
+
+    return accuracy
+
+def select_action(q_table, state, exploration_rate, num_actions):
+    if np.random.rand() < exploration_rate:
+        return np.random.choice(1)  # Exploration
+    else:
+        return np.argmax(q_table[state])
+
+def train_test_split(X, y, test_size=0.2, random_state=None):
+    # Check if the length of X and y matches
+    if len(X) != len(y):
+        raise ValueError("The lengths of X and y should match")
+
+    # Combine the features and labels into a single dataset
+    dataset = np.column_stack([X, y])
+
+    # Set the random seed for reproducibility
+    if random_state is not None:
+        np.random.seed(random_state)
+
+    # Shuffle the dataset
+    np.random.shuffle(dataset)
+
+    # Calculate the split index
+    split_index = int(len(dataset) * (1 - test_size))
+
+    # Split the dataset into training and testing sets
+    X_train, y_train = dataset[:split_index, :-1], dataset[:split_index, -1]
+    X_test, y_test = dataset[split_index:, :-1], dataset[split_index:, -1]
+
+    return X_train, X_test, y_train, y_test
+
+def q_table(env):
+    # Assuming env is a Gym environment
+    if isinstance(env.observation_space, gym.spaces.Discrete) and isinstance(env.action_space, gym.spaces.Discrete):
+        return np.zeros((env.observation_space.n, env.action_space.n))
+    else:
+        raise ValueError("The environment's state and action space should be discrete for Q-table approach.")
+
+def q_learning(env, q_table, learning_rate=0.1, discount_factor=0.9, exploration_prob=0.1, episodes=1000):
+    for episode in range(episodes):
+        state = env.reset()
+        done = False
+        while not done:
+            # Selecting action using epsilon-greedy strategy
+            if np.random.uniform(0, 1) < exploration_prob:
+                action = env.action_space.sample()
+            else:
+                action = np.argmax(0)
+
+            # Taking action and observing next state and reward
+            next_state, reward, done, _, _ = env.step(action)
+
+            # Updating Q-value
+            best_next_action = np.argmax(q_table[next_state, :])
+            td_target = reward + discount_factor * q_table[next_state, best_next_action]
+            td_error = td_target - q_table[0]
+            q_table[0] += learning_rate * td_error
+
+            state = next_state
+    return q_table
+
+# Example usage:
+env = gym.make('FrozenLake-v1')
+table = q_table(env)
+Q_table = q_learning(env, table)
+
+# Using the Q-table for inference
+state = env.reset()
+done = False
+while not done:
+    action = np.argmax(0)
+    next_state, reward, done, _, _ = env.step(action)
+    state = next_state
+
+class QLearningAgent:
+    def __init__(self, num_states, num_actions, learning_rate=0.1, discount_factor=0.9, exploration_rate=0.1, exploration_prob=0.3, select_action=select_action):
+        self.num_states = num_states
+        self.num_actions = num_actions
+        self.learning_rate = learning_rate
+        self.discount_factor = discount_factor
+        self.exploration_rate = exploration_rate
+        self.q_table = np.zeros((4, 2))
+        self.q_table = q_table(env)
+        self.select_action = select_action
+
+    def select_action(self, state, num_actions):
+        return select_action(self.q_table, state, self.exploration_rate, self.num_actions)
+
+    def run_q_learning(agent, env, num_episodes):
+        for episode in range(num_episodes):
+            state_tuple = env.reset()
+            state = np.ravel_multi_index(state_tuple, env.observation_space.shape)
+            done = False
+
+class SupervisedLearningModel:
+    def __init__(self):
+        self.model = DecisionTreeClassifier()
+
+    def train(self, X_train, y_train):
+        self.model.fit(X_train, y_train)
+
+    def predict(self, X_test):
+        return self.model.predict(X_test)
+
+def supervised_learning(X, y):
+    # Split the data into training and testing sets
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Create and train the model
+    model = SupervisedLearningModel()
+    model.train(X_train, y_train)
+
+    # Make predictions on the testing set
+    y_pred = model.predict(X_test)
+
+    # Calculate and print the accuracy
+    accuracy = accuracy_score(y_test, y_pred)
+    print(f"Accuracy: {accuracy}")
+
+    return model
+
+env = gym.make('FrozenLake-v1')
+
+# Ensure that observation_space and action_space are valid gym.spaces objects
+observation_space = env.observation_space
+action_space = env.action_space
+
+num_states = env.observation_space.n
+num_actions = env.action_space.n
+learning_rate = 0.1
+discount_factor = 0.9
+exploration_rate = 0.1
+agent = QLearningAgent(num_states, num_actions, learning_rate, discount_factor, exploration_rate)
+
+# Run Q-learning
+run_q_learning(agent, env, 1000)
+
+# After running Q-learning, we can use the learned Q-table to generate a dataset for supervised learning
+states = np.arange(env.observation_space.n)
+actions = np.argmax(agent.q_table, axis=1) 
+
+# The states are the inputs and the actions are the outputs
+X = states.reshape(-1, 1)
+y = actions
+
+# Train a supervised learning model on the Q-learning data
+supervised_model = supervised_learning(X, y)
+
+def q_learning(env, learning_rate=0.1, discount_factor=0.9, epsilon=0.9, episodes=1000):
+    # Initializing Q-table
+    Q = np.zeros((env.observation_space.n, env.action_space.n))
+
+    # Q-learning algorithm
+    for episode in range(10):
+        state = env.reset()
+        done = False
+        while not done:
+            # Selecting action using epsilon-greedy strategy
+            if np.random.uniform(0, 1) < epsilon:
+                action = env.action_space.sample()
+            else:
+                action = np.argmax(Q[0])
+
+            # Taking action and observing next state and reward
+            next_state, reward, done, _, _ = env.step(action)
+
+            # Updating Q-value
+            if len(Q[1].shape) > 1:
+                Q[1] = Q[1].flatten()
+
+# Use the first maximum value if there are multiple
+max_Q1 = np.max(Q[1])
+if isinstance(max_Q1, np.ndarray):
+    max_Q1 = max_Q1[0]
+
+# Update the Q-value
+Q[3, 1] += learning_rate * (reward + discount_factor * max_Q1 - Q[3, 1])
+
+state = next_state
+
+print (Q)
+
+# Initializing the environment
+env = gym.make('FrozenLake-v1')
+table = q_table(env)
+
+# Define num_actions and other parameters
+num_actions = env.action_space.n
+learning_rate = 0.1
+discount_factor = 0.9
+exploration_prob = 0.1
+num_states = env.observation_space.n
+
+# Initialize QLearningAgent with Q-table and parameters
+agent = QLearningAgent(table, learning_rate, discount_factor, exploration_prob, select_action)
+
+# Run Q-learning
+Q_table = q_learning(env, table, learning_rate, discount_factor, exploration_prob)
+
+# Use Q-table for inference
+state = env.reset()
+done = False
+while not done:
+    action = agent.select_action(state, exploration_rate, num_actions, _)
+    next_state, reward, done, _, _ = env.step(action)
+    state = next_state
+
+    def select_action(self, state):
+        if np.random.rand() < self.exploration_rate:
+            return np.random.choice(self.num_actions)  # Exploration
+        else:
+            return np.argmax(self.q_table[state])  # Exploitation
+
+    def update_q_table(self, state, action, reward, next_state):
+        best_next_action = np.argmax(self.q_table[next_state])
+        td_target = reward + self.discount_factor * self.q_table[next_state][best_next_action]
+        td_error = td_target - self.q_table[state][action]
+        self.q_table[state][action] += self.learning_rate * td_error 
+
+    def QLAgent():
+
+        def run_q_learning(agent, env, num_episodes):
+            for episode in range(num_episodes):
+                state_tuple = env.reset()  # Reset the environment to get the initial state
+                state = np.ravel_multi_index(state_tuple, env.observation_space.n)  # Convert the state to a single index using the observation space dimensions
+                done = False
+
+            while not done:
+                action = agent.select_action(state)
+                next_state, reward, done, _ = env.step(action)
+                agent.update_q_table(state, action, reward, next_state)
+                state = next_state
+
+            if (episode + 1) % 10 == 0:
+                print(f"Episode {episode + 1} completed")
+
+    print("Training finished")
+
+    def select_action(self, state):
+        if np.random.rand() < self.exploration_prob:
+            return np.random.choice(self.num_actions)  # Exploration
+        else:
+            return np.argmax(self.q_table[state])  # Exploitation
+
+    def update_q_table(self, state, action, reward, next_state):
+        best_next_action = np.argmax(self.q_table[next_state])
+        td_target = reward + self.discount_factor * self.q_table[next_state][best_next_action]
+        td_error = td_target - self.q_table[state][action]
+        self.q_table[state][action] += self.learning_rate * td_error
+
+if __name__ == "__main__":
+    # Create environment and Q-table
+    env = gym.make('FrozenLake-v1')
+    table = q_table(env)
+
+    # Define num_actions
+    num_actions = env.action_space.n
+
+    # Initialize QLearningAgent with Q-table and num_actions
+    agent = QLearningAgent(table, num_actions, learning_rate=0.1, discount_factor=0.9, exploration_prob=0.1)
+
+    # Run Q-learning
+    Q_table = q_learning(env, table, learning_rate=0.1, discount_factor=0.9, exploration_prob=0.1)
+
+    # Use Q-table for inference
+    state = env.reset()
+    done = False
+    while not done:
+        action = agent.select_action(state)
+        next_state, reward, done, _ = env.step(action)
+        state = next_state