Policy Iteration für die optimale Policy anwenden

Policy Iteration ist eine grundlegende Technik im RL, um eine optimale Policy zu finden. Sie besteht aus zwei Schritten: der Policy-Bewertung, bei der du die Zustandswertfunktion für eine gegebene Policy berechnest, und der Policy-Verbesserung, bei der du die Policy auf Basis dieser Werte aktualisierst. Du wendest diese Schritte iterativ an, um im benutzerdefinierten MyGridWorld-Environment zur optimalen Policy zu konvergieren.

Die Funktion render_policy() zeigt die Schritte, die ein Agent gemäß einer Policy ausführt.

Die Funktionen compute_state_value(state, policy) und compute_q_value(state, action, policy) wurden für dich vorab geladen.

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Reinforcement Learning mit Gymnasium in Python

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# Complete the policy evaluation function
def policy_evaluation(policy):
    V = {____: ____ for ____ in range(____)}
    return V

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Diese Übung ist Teil des Kurses

Reinforcement Learning mit Gymnasium in Python

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

Aktuelle Übung

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!