diff --git a/sol1.py b/sol1.py
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+++ b/sol1.py
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+#!/usr/bin/env python
+import gymnasium as gym
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+
+def initialize_environment():
+    """Initialisiere die FrozenLake Umgebung (deterministisch)."""
+    env = gym.make('FrozenLake-v1', is_slippery=False)
+    return env
+
+
+def initialize_q_table(n_states, n_actions):
+    """Initialisiere Q-Tabelle mit Nullen."""
+    return np.zeros((n_states, n_actions))
+
+
+def select_action(state, Q, epsilon, n_actions):
+    """
+    Wähle Aktion mit ε-greedy Strategie.
+    
+    Args:
+        state: Aktueller Zustand
+        Q: Q-Tabelle
+        epsilon: Explorations-Wahrscheinlichkeit
+        n_actions: Anzahl möglicher Aktionen
+    
+    Returns:
+        Gewählte Aktion
+    """
+    if np.random.random() < epsilon:
+        # Exploration: Zufällige Aktion
+        return np.random.randint(n_actions)
+    else:
+        # Exploitation: Beste bekannte Aktion
+        return np.argmax(Q[state, :])
+
+
+def q_learning_update(Q, state, action, reward, next_state, alpha, gamma, done):
+    """
+    Führe Q-Learning Update aus.
+    
+    Q(s,a) ← Q(s,a) + α · [r + γ · max Q(s',a') - Q(s,a)]
+    
+    Args:
+        Q: Q-Tabelle
+        state: Aktueller Zustand
+        action: Ausgeführte Aktion
+        reward: Erhaltene Belohnung
+        next_state: Nächster Zustand
+        alpha: Lernrate
+        gamma: Diskontierungsfaktor
+        done: Episode beendet?
+    """
+    # Beste nächste Aktion
+    best_next_action = np.argmax(Q[next_state, :])
+    
+    # TD Target
+    if done:
+        td_target = reward  # Keine zukünftigen Belohnungen
+    else:
+        td_target = reward + gamma * Q[next_state, best_next_action]
+    
+    # TD Error
+    td_error = td_target - Q[state, action]
+    
+    # Update Q-Wert
+    Q[state, action] += alpha * td_error
+
+
+def train_q_learning(env, n_episodes=2000, alpha=0.1, gamma=0.99, 
+                     epsilon_start=1.0, epsilon_min=0.01, epsilon_decay=0.999):
+    """
+    Trainiere Q-Learning Agent.
+    
+    Args:
+        env: Gymnasium Environment
+        n_episodes: Anzahl Trainings-Episoden
+        alpha: Lernrate
+        gamma: Diskontierungsfaktor
+        epsilon_start: Initiale Explorations-Rate
+        epsilon_min: Minimale Explorations-Rate
+        epsilon_decay: Explorations-Abnahme-Rate
+    
+    Returns:
+        Q: Trainierte Q-Tabelle
+        rewards: Liste der Episoden-Belohnungen
+    """
+    # Initialisierung
+    n_states = env.observation_space.n
+    n_actions = env.action_space.n
+    Q = initialize_q_table(n_states, n_actions)
+    
+    epsilon = epsilon_start
+    rewards = []
+    
+    # Trainingsschleife
+    for episode in range(n_episodes):
+        state, _ = env.reset()
+        done = False
+        episode_reward = 0
+        
+        while not done:
+            # Aktion wählen (ε-greedy)
+            action = select_action(state, Q, epsilon, n_actions)
+            
+            # Aktion ausführen
+            next_state, reward, terminated, truncated, _ = env.step(action)
+            done = terminated or truncated
+            episode_reward += reward
+            
+            # Q-Learning Update
+            q_learning_update(Q, state, action, reward, next_state, alpha, gamma, done)
+            
+            # Zustand updaten
+            state = next_state
+        
+        # Epsilon Decay
+        epsilon = max(epsilon_min, epsilon * epsilon_decay)
+        rewards.append(episode_reward)
+        
+        # Fortschritt ausgeben
+        if (episode + 1) % 100 == 0:
+            avg_reward = np.mean(rewards[-100:])
+            print(f"Episode {episode+1}/{n_episodes}, Avg Belohnung (letzte 100): {avg_reward:.2f}, ε: {epsilon:.3f}")
+    
+    return Q, rewards
+
+
+def evaluate_policy(env, Q, n_episodes=100):
+    """
+    Evaluiere gelernte Policy ohne Exploration.
+    
+    Args:
+        env: Gymnasium Environment
+        Q: Trainierte Q-Tabelle
+        n_episodes: Anzahl Evaluations-Episoden
+    
+    Returns:
+        success_rate: Erfolgsrate in Prozent
+        avg_reward: Durchschnittliche Belohnung pro Episode
+    """
+    successes = 0
+    total_reward = 0
+    
+    for episode in range(n_episodes):
+        state, _ = env.reset()
+        done = False
+        episode_reward = 0
+        
+        while not done:
+            # Greedy Aktion (keine Exploration)
+            action = np.argmax(Q[state, :])
+            state, reward, terminated, truncated, _ = env.step(action)
+            done = terminated or truncated
+            episode_reward += reward
+        
+        total_reward += episode_reward
+        if episode_reward > 0:
+            successes += 1
+    
+    success_rate = (successes / n_episodes) * 100
+    avg_reward = total_reward / n_episodes
+    
+    return success_rate, avg_reward
+
+
+def plot_learning_curve(rewards, window=100):
+    """
+    Plotte Lernkurve mit gleitendem Durchschnitt.
+    
+    Args:
+        rewards: Liste der Episoden-Belohnungen
+        window: Fenstergrösse für gleitenden Durchschnitt
+    """
+    plt.figure(figsize=(10, 6))
+    
+    plt.plot(rewards, alpha=0.3, label='Episoden-Belohnung')
+    
+    # Gleitender Durchschnitt
+    if len(rewards) >= window:
+        moving_avg = np.convolve(rewards, np.ones(window)/window, mode='valid')
+        plt.plot(range(window-1, len(rewards)), moving_avg, 
+                label=f'Gleitender Durchschnitt ({window} Episoden)', linewidth=2)
+    
+    plt.xlabel('Episode')
+    plt.ylabel('Belohnung')
+    plt.title('Lernkurve')
+    plt.legend(loc='lower right')
+    plt.grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    plt.show()
+
+
+def visualize_q_table(Q):
+    """
+    Visualisiere Q-Tabelle als Heatmap.
+    
+    Args:
+        Q: Q-Tabelle
+    """
+    plt.figure(figsize=(10, 8))
+    sns.heatmap(Q, annot=True, fmt='.2f', cmap='YlOrRd',
+                xticklabels=['←', '↓', '→', '↑'],
+                yticklabels=[str(i) for i in range(Q.shape[0])])
+    plt.title('Q-Tabelle nach Training')
+    plt.xlabel('Aktion')
+    plt.ylabel('Zustand')
+    plt.tight_layout()
+    plt.show()
+
+
+def visualize_policy(Q):
+    """
+    Visualisiere gelernte Policy auf dem Grid.
+    
+    Args:
+        Q: Q-Tabelle
+    """
+    policy = np.argmax(Q, axis=1)
+    action_arrows = {0: '←', 1: '↓', 2: '→', 3: '↑'}
+    
+    print("\n" + "="*40)
+    print("GELERNTE POLICY")
+    print("="*40)
+    print("\nFrozenLake Grid:")
+    print("SFFF")
+    print("FHFH")
+    print("FFFH")
+    print("HFFG")
+    print("\nOptimale Aktionen:")
+    
+    policy_grid = policy.reshape(4, 4)
+    for i, row in enumerate(policy_grid):
+        row_str = ' '.join([action_arrows[a] for a in row])
+        print(f"Reihe {i}: {row_str}")
+    
+    print("\nZustand-für-Zustand Policy:")
+    for state in range(16):
+        row, col = state // 4, state % 4
+        print(f"Zustand {state:2d} (Reihe {row}, Spalte {col}): {action_arrows[policy[state]]}")
+    print("="*40 + "\n")
+
+
+
+
+
+def main():
+    """Hauptfunktion für alle Aufgaben aus Übung 1."""
+    
+    print("="*60)
+    print("Q-LEARNING FÜR FROZENLAKE (DETERMINISTISCH)")
+    print("Praktische Übung 1 - Lösung")
+    print("="*60)
+    
+    # Environment initialisieren
+    env = initialize_environment()
+    n_states = env.observation_space.n  # type: ignore
+    n_actions = env.action_space.n  # type: ignore
+    print(f"\nEnvironment: FrozenLake-v1 (is_slippery=False)")
+    print(f"Zustände: {n_states}")
+    print(f"Aktionen: {n_actions}")
+    
+    # ========================================
+    # HYPERPARAMETER - Optimal getunt für deterministisches FrozenLake
+    # ========================================
+    n_episodes = 3000
+    alpha = 0.4          # Lernrate
+    gamma = 0.99         # Diskontierung
+    epsilon_start = 1.0
+    epsilon_min = 0.01
+    epsilon_decay = 0.999  # Optimal für deterministisches Environment
+    
+    # Q-Learning trainieren
+    print("\n--- Q-Learning Training ---")
+    print(f"Hyperparameter: α={alpha}, γ={gamma}, ε_decay={epsilon_decay}")
+    Q, rewards = train_q_learning(
+        env, 
+        n_episodes=n_episodes,
+        alpha=alpha,
+        gamma=gamma,
+        epsilon_start=epsilon_start,
+        epsilon_min=epsilon_min,
+        epsilon_decay=epsilon_decay
+    )
+    
+    # Lernkurve plotten
+    print("\n--- Lernkurve ---")
+    plot_learning_curve(rewards)
+    
+    # Policy evaluieren
+    print("\n--- Policy Evaluation ---")
+    success_rate, avg_reward = evaluate_policy(env, Q, n_episodes=100)
+    print(f"Erfolgsrate (100 Episoden): {success_rate:.1f}%")
+    print(f"Durchschnittliche Belohnung: {avg_reward:.3f}")
+    
+    # Visualisierungen
+    print("\n--- Visualisierungen ---")
+    visualize_q_table(Q)
+    visualize_policy(Q)
+    
+    print("\n" + "="*60)
+    print("TRAINING ABGESCHLOSSEN!")
+    print("="*60)
+    print("\nÄndere die Hyperparameter oben und führe erneut aus!")
+    print("  - Versuche α = 0.01 oder α = 0.5")
+    print("  - Versuche γ = 0.5 oder γ = 0.99")
+    print("  - Versuche epsilon_decay = 1.0 (kein Decay)")
+    
+    env.close()
+
+
+if __name__ == "__main__":
+    main()