Episodenerzeugung für Monte-Carlo-Methoden

Monte-Carlo-Methoden benötigen erzeugte Episoden, um die Wertfunktion abzuleiten. Deshalb implementierst du jetzt eine Funktion, die Episoden generiert, indem sie zufällig Aktionen auswählt, bis eine Episode endet. In späteren Aufgaben wirst du diese Funktion aufrufen, um Monte-Carlo-Methoden auf die für dich vorab geladene benutzerdefinierte Umgebung env anzuwenden.

Die Funktion render() ist für dich vorab geladen.

Diese Übung ist Teil des Kurses

Reinforcement Learning mit Gymnasium in Python

Anleitung zur Übung

Setze die Umgebung mit einem seed von 42 zurück.
Wähle in der Episoden-Schleife in jeder Iteration eine zufällige action.
Sobald eine Iteration endet, aktualisiere die Episoden-Daten, indem du das Tupel (state, action, reward) hinzufügst.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

def generate_episode():
    episode = []
    # Reset the environment
    state, info = ____
    terminated = False
    while not terminated:
      # Select a random action
      action = ____
      next_state, reward, terminated, truncated, info = env.step(action)
      render()
      # Update episode data
      episode.____(____)
      state = next_state
    return episode
print(generate_episode())

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

Reinforcement Learning mit Gymnasium in Python

Hohe SchwierigkeitSchwierigkeitsgrad

4.8+

Kurs kostenlos starten

Dive into the exciting world of Reinforcement Learning (RL) by exploring its foundational concepts, roles, and applications. Navigate through the RL framework, uncovering the agent-environment interaction. You'll also learn how to use the Gymnasium library to create environments, visualize states, and perform actions, thus gaining a practical foundation in RL concepts and applications.

Exercise 1: Fundamentals of reinforcement learning Exercise 2: What is Reinforcement Learning?Exercise 3: RL vs. other ML sub-domains Exercise 4: Scenarios for applying RL Exercise 5: Navigating the RL framework Exercise 6: RL interaction loop Exercise 7: Episodic and continuous RL tasks Exercise 8: Calculating discounted returns for agent strategies Exercise 9: Interacting with Gymnasium environments Exercise 10: Setting up a Mountain Car environment Exercise 11: Visualizing the Mountain Car Environment Exercise 12: Interacting with the Frozen Lake environment

Delve deeper into the world of RL focusing on model-based learning. Unravel the complexities of Markov Decision Processes (MDPs), understanding their essential components. Enhance your skill set by learning about policies and value functions. Gain expertise in policy optimization with policy iteration and value Iteration techniques.

Exercise 1: Markov Decision Processes Exercise 2: Custom Frozen Lake MDP components Exercise 3: Exploring state and action spaces Exercise 4: Transition probabilities and rewards Exercise 5: Policies and state-value functions Exercise 6: Defining a deterministic policy Exercise 7: Computing state-values for a policy Exercise 8: Comparing policies Exercise 9: Action-value functions Exercise 10: Computing Q-values Exercise 11: Improving a policy Exercise 12: Policy iteration and value iteration Exercise 13: Applying policy iteration for optimal policy Exercise 14: Implementing value iteration

Embark on a journey through the dynamic realm of Model-Free Learning in RL. Get introduced to to the foundational Monte Carlo methods, and apply first-visit and every-visit Monte Carlo prediction algorithms. Transition into the world of Temporal Difference Learning, exploring the SARSA algorithm. Finally, dive into the depths of Q-Learning, and analyze its convergence in challenging environments.

Exercise 1: Monte-Carlo-Methoden Exercise 2: Episodenerzeugung für Monte-Carlo-Methoden

Aktuelle Übung

Exercise 3: First-Visit Monte Carlo implementieren Exercise 4: Every-Visit Monte Carlo implementieren Exercise 5: Temporal-Differenzen-Lernen Exercise 6: Die SARSA-Aktualisierungsregel implementieren Exercise 7: 8x8 Frozen Lake mit SARSA lösen Exercise 8: Q-Learning Exercise 9: Q-Learning-Aktualisierungsregel implementieren Exercise 10: 8x8 Frozen Lake mit Q-Learning lösen Exercise 11: Richtlinie auf einem rutschigen Frozen Lake evaluieren

Dive into advanced strategies in Model-Free RL, focusing on enhancing decision-making algorithms. Learn about Expected SARSA for more accurate policy updates and Double Q-learning to mitigate overestimation bias. Explore the Exploration-Exploitation Tradeoff, mastering epsilon-greedy and epsilon-decay strategies for optimal action selection. Tackle the Multi-Armed Bandit Problem, applying strategies to solve decision-making challenges under uncertainty.

Exercise 1: Expected SARSA Exercise 2: Expected SARSA update rule Exercise 3: Applying Expected SARSA Exercise 4: Double Q-learning Exercise 5: Implementing double Q-learning update rule Exercise 6: Applying double Q-learning Exercise 7: Balancing exploration and exploitation Exercise 8: Defining epsilon-greedy function Exercise 9: Solving CliffWalking with epsilon greedy strategy Exercise 10: Solving CliffWalking with decayed epsilon-greedy strategy Exercise 11: Multi-armed bandits Exercise 12: Creating a multi-armed bandit Exercise 13: Solving a multi-armed bandit Exercise 14: Assessing convergence in a multi-armed bandit Exercise 15: Congratulations!