Epsilon-greedy-functie definiëren

In RL is de epsilon-greedystrategie een balans tussen exploratie en exploitatie. Deze methode kiest met kans epsilon een willekeurige actie en met kans 1-epsilon de best bekende actie. Het implementeren van de functie epsilon_greedy() is cruciaal voor algoritmen zoals Q-learning en SARSA. Het helpt het leerproces van de agent door zowel de omgeving te verkennen als bekende beloningen te benutten, en dat is het doel van deze oefening.

De numpy-bibliotheek is geïmporteerd als np.

Deze oefening maakt deel uit van de cursus

Reinforcement Learning met Gymnasium in Python

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Oefeninstructies

Schrijf binnen de functie de juiste voorwaarde voor een agent om de omgeving te verkennen.
Kies bij verkennen een willekeurige action.
Kies bij benutten de beste action volgens de q_table.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

epsilon = 0.2
env = gym.make('FrozenLake')
q_table = np.random.rand(env.observation_space.n, env.action_space.n)

def epsilon_greedy(state):
    # Implement the condition to explore
    if ____ < ____:
      	# Choose a random action
        action = ____
    else:
      	# Choose the best action according to q_table
        action = ____
    return action

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Reinforcement Learning met Gymnasium in Python

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4.8+

Begin de cursus gratis

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-update-regel Exercise 3: Expected SARSA toepassen Exercise 4: Double Q-learning Exercise 5: Update-regel van Double Q-learning implementeren Exercise 6: Double Q-learning toepassen Exercise 7: Balans tussen exploratie en exploitatie Exercise 8: Epsilon-greedy-functie definiëren

Huidige oefening

Exercise 9: CliffWalking oplossen met de epsilon-greedystrategie Exercise 10: CliffWalking oplossen met een afnemende epsilon-greedystrategie Exercise 11: Multi-armed bandits Exercise 12: Een multi-armed bandit maken Exercise 13: Een multi-armed bandit oplossen Exercise 14: Convergentie beoordelen in een multi-armed bandit Exercise 15: Gefeliciteerd!