Expected SARSA anwenden

Jetzt wendest du den Expected-SARSA-Algorithmus in einer benutzerdefinierten Umgebung an, wie unten gezeigt. Ziel ist es, einen Agenten durch ein Raster zu navigieren, damit er das Ziel so schnell wie möglich erreicht. Es gelten die gleichen Regeln wie zuvor: Der Agent erhält eine Belohnung von +10 beim Erreichen des Diamanten, -2 beim Durchqueren eines Bergfeldes und -1 für jeden anderen Zustand.

Die Umgebung wurde als env importiert.

Diese Übung ist Teil des Kurses

Reinforcement Learning mit Gymnasium in Python

Anleitung zur Übung

Initialisiere die Q-Tabelle Q mit Nullen für jedes Zustands-Aktions-Paar.
Aktualisiere die Q-Tabelle mit der Funktion update_q_table().
Leite die Policy als Dictionary aus der gelernten Q-Tabelle ab.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Initialize the Q-table with random values
Q = ____
for i_episode in range(num_episodes):
    state, info = env.reset()    
    done = False    
    while not done: 
        action = env.action_space.sample()               
        next_state, reward, done, truncated, info = env.step(action)
        # Update the Q-table
        ____
        state = next_state
# Derive policy from Q-table        
policy = {state: ____ for state in range(____)}
render_policy(policy)

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 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-Aktualisierungsregel Exercise 3: Expected SARSA anwenden

Aktuelle Übung

Exercise 4: Double Q-Learning Exercise 5: Implementiere die Double-Q-Learning-Aktualisierungsregel Exercise 6: Double Q-learning anwenden Exercise 7: Exploration und Exploitation ausbalancieren Exercise 8: Epsilon-greedy-Funktion definieren Exercise 9: CliffWalking mit epsilon-gieriger Strategie lösen Exercise 10: CliffWalking mit abgeklungener Epsilon-greedy-Strategie lösen Exercise 11: Multi-armed Bandits Exercise 12: Einen Multi-Armed-Bandit erstellen Exercise 13: Einen Multi-Armed Bandit lösen Exercise 14: Konvergenz in einem Multi-Armed-Bandit beurteilen Exercise 15: Glückwunsch!