Een Mountain Car-omgeving opzetten

Een van de meest gebruikte Gym-omgevingen is Mountain Car, waarin het doel is om met een zwakke auto een steile heuvel op te rijden. De motor is niet krachtig genoeg om de heuvel in één keer te beklimmen, dus de auto moet vaart opbouwen door heen en weer te rijden. Jij gaat deze omgeving aanmaken en klaarzetten.

Deze oefening maakt deel uit van de cursus

Reinforcement Learning met Gymnasium in Python

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Oefeninstructies

Importeer de bibliotheek gymnasium als gym.
Maak een Mountain Car-omgeving met de Gym-bibliotheek met de environment-ID MountainCar en stel render_mode in op 'rgb_array'.
Reset de omgeving met een seed van 42 en haal de initial_state op, die twee waarden bevat: de positie en de snelheid van de auto.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import the gymnasium library
____

# Create the environment
env = ____

# Get the initial state
initial_state, info = ____

position = initial_state[0]
velocity = initial_state[1]

print(f"The position of the car along the x-axis is {position} (m)")
print(f"The velocity of the car is {velocity} (m/s)")

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Reinforcement Learning met Gymnasium in Python

SkillTag.level.advancedSkillTag.label

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: Basisprincipes van reinforcement learning Exercise 2: Wat is Reinforcement Learning?Exercise 3: RL vs. andere ML-subdomeinen Exercise 4: Scenario's voor het toepassen van RL Exercise 5: Navigeren door het RL-framework Exercise 6: RL-interactielus Exercise 7: Episodische en continue RL-taken Exercise 8: Gedisconteerde returns berekenen voor agentstrategieën Exercise 9: Interactie met Gymnasium-omgevingen Exercise 10: Een Mountain Car-omgeving opzetten

Huidige oefening

Exercise 11: De Mountain Car-omgeving visualiseren Exercise 12: Interactie met de Frozen Lake-omgeving

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 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!