Die SARSA-Aktualisierungsregel implementieren

SARSA ist ein On-Policy-Algorithmus im RL, der die Aktionswertfunktion anhand der gewählten Aktion und der im nächsten Zustand ausgewählten Aktion aktualisiert. Dieses Verfahren hilft, nicht nur den Wert des aktuellen Zustand-Aktions-Paares zu lernen, sondern auch den des folgenden, und ermöglicht so das Erlernen von Policies, die zukünftige Aktionen berücksichtigen. Unten findest du die SARSA-Aktualisierungsregel; deine Aufgabe ist es, eine Funktion zu implementieren, die eine Q-Tabelle basierend auf dieser Regel aktualisiert.

Die NumPy-Bibliothek wurde als np importiert.

Image showing the mathematical formula of the SARSA update rule.

Diese Übung ist Teil des Kurses

Reinforcement Learning mit Gymnasium in Python

Anleitung zur Übung

Hole den aktuellen Q-Wert für das gegebene Zustand-Aktions-Paar.
Ermittle den Q-Wert für das nächste Zustand-Aktions-Paar.
Aktualisiere den Q-Wert für das aktuelle Zustand-Aktions-Paar mithilfe der SARSA-Formel.
Aktualisiere die Q-Tabelle Q, gegeben, dass ein Agent in Zustand 0 die Aktion 0 ausführt, eine Belohnung von 5 erhält, zu Zustand 1 wechselt und dort Aktion 1 ausführt.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

def update_q_table(state, action, reward, next_state, next_action):
  	# Get the old value of the current state-action pair
    old_value = ____
    # Get the value of the next state-action pair
    next_value = ____
    # Compute the new value of the current state-action pair
    Q[(state, action)] = ____

alpha = 0.1
gamma  = 0.8
Q = np.array([[10,0],[0,20]], dtype='float32')
# Update the Q-table for the ('state1', 'action1') pair
____
print(Q)

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Diese Übung ist Teil des Kurses

Reinforcement Learning mit Gymnasium in Python

Hohe SchwierigkeitSchwierigkeitsgrad

4.8+

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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 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

Aktuelle Übung

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!