Das Mountain-Car-Environment visualisieren

Jetzt gehst du bei der Erkundung der Mountain-Car-Umgebung einen Schritt weiter. Visualisierung ist entscheidend, um die Dynamik von RL-Umgebungen zu verstehen. Du schreibst eine Funktion render(), die den aktuellen Zustand der Umgebung anzeigt. Diese Funktion wirst du später für jede Umgebung verwenden, die du visualisieren möchtest.

matplotlib.pyplot und gymnasium wurden als plt bzw. gym importiert.

Diese Übung ist Teil des Kurses

Reinforcement Learning mit Gymnasium in Python

Anleitung zur Übung

Vervollständige die Funktion render(), um die Umgebung zu visualisieren, indem du das state_image der Umgebung erhältst und es plottest.
Rufe die Funktion render() auf, um den aktuellen Zustand der Umgebung anzuzeigen.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

env = gym.make('MountainCar', render_mode='rgb_array')
initial_state, _ = env.reset()

# Complete the render function
def render():
    state_image = ____
    ____
    plt.show()

# Call the render function    
____

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: Grundlagen des Reinforcement Learning Exercise 2: Was ist Reinforcement Learning?Exercise 3: RL vs. andere ML-Teildisziplinen Exercise 4: Szenarien für den Einsatz von RL Exercise 5: Das RL-Framework verstehen und anwenden Exercise 6: RL-Interaktionsschleife Exercise 7: Episodische und kontinuierliche RL‑Aufgaben Exercise 8: Abgezinste Returns für Agentenstrategien berechnen Exercise 9: Mit Gymnasium-Umgebungen interagieren Exercise 10: Eine Mountain-Car-Umgebung einrichten Exercise 11: Das Mountain-Car-Environment visualisieren

Aktuelle Übung

Exercise 12: Interaktion mit der Frozen-Lake-Umgebung

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 methods Exercise 2: Episode generation for Monte Carlo methods Exercise 3: Implementing first-visit Monte Carlo Exercise 4: Implementing every-visit Monte Carlo Exercise 5: Temporal difference learning Exercise 6: Implementing the SARSA update rule Exercise 7: Solving 8x8 Frozen Lake with SARSA Exercise 8: Q-learning Exercise 9: Implementing Q-learning update rule Exercise 10: Solving 8x8 Frozen Lake with Q-learning Exercise 11: Evaluating policy on a slippery Frozen Lake

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!