Zustands- und Aktionsräume erkunden

Im Cliff-Walking-Umfeld überquert ein Agent eine Gitterwelt vom Start bis zum Ziel und vermeidet dabei den Abgrund. Bewegt sich der Spieler auf eine Klippenposition, kehrt er zum Startpunkt zurück. Der Spieler führt Züge aus, bis er das Ziel erreicht; dann endet die Episode. Deine Aufgabe ist es, die Zustands- und Aktionsräume dieses Umfelds zu untersuchen.

Image showing an animation for the cliff walking environment.

Diese Übung ist Teil des Kurses

Reinforcement Learning mit Gymnasium in Python

Anleitung zur Übung

Erzeuge eine Umgebungsinstanz für Cliff Walking mit der Umgebungs-ID CliffWalking.
Berechne die Größe des Aktionsraums und speichere sie in num_actions.
Berechne die Größe des Zustandsraums und speichere sie in num_states.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Create the Cliff Walking environment
env = ____

# Compute the size of the action space
num_actions = ____

# Compute the size of the state space
num_states = ____

print("Number of actions:", num_actions)
print("Number of states:", num_states)

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: Markow-Entscheidungsprozesse Exercise 2: Eigene MDP-Komponenten für Frozen Lake Exercise 3: Zustands- und Aktionsräume erkunden

Aktuelle Übung

Exercise 4: Übergangswahrscheinlichkeiten und Belohnungen Exercise 5: Policies und Zustandswertfunktionen Exercise 6: Eine deterministische Policy definieren Exercise 7: Zustandswerte für eine Policy berechnen Exercise 8: Policies vergleichen Exercise 9: Aktionswertfunktionen Exercise 10: Q-Werte berechnen Exercise 11: Eine Policy verbessern Exercise 12: Policy Iteration und Value Iteration Exercise 13: Policy Iteration für die optimale Policy anwenden Exercise 14: Value Iteration implementieren

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!