Menerapkan aturan pembaruan Q-learning

Q-learning adalah algoritme off-policy dalam reinforcement learning (RL) yang bertujuan menemukan aksi terbaik yang diambil berdasarkan state saat ini. Berbeda dengan SARSA, yang mempertimbangkan aksi berikutnya yang benar-benar diambil, Q-learning memperbarui nilai Q-nya menggunakan imbalan masa depan maksimum tanpa memperhatikan aksi yang diambil. Perbedaan ini memungkinkan Q-learning mempelajari kebijakan optimal sekaligus mengikuti kebijakan eksploratif atau bahkan acak. Berikut tugas untuk mengimplementasikan fungsi yang memperbarui Q-table berdasarkan aturan Q-learning. Aturan pembaruan Q-learning ada di bawah, dan tugas Anda adalah mengimplementasikan fungsi yang memperbarui Q-table berdasarkan aturan tersebut.

Pustaka NumPy telah diimpor sebagai np.

Image showing the mathematical formula of the Q-learning update rule.

Latihan ini adalah bagian dari kursus

Reinforcement Learning dengan Gymnasium di Python

Petunjuk latihan

Ambil nilai Q saat ini untuk pasangan state-aksi yang diberikan.
Tentukan nilai Q maksimum untuk state berikutnya di seluruh aksi yang mungkin dalam actions.
Perbarui nilai Q untuk pasangan state-aksi saat ini menggunakan rumus Q-learning.
Perbarui Q-table Q, dengan ketentuan agen mengambil aksi 0 pada state 0, menerima imbalan 5, dan berpindah ke state 1.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

actions = ['action1', 'action2'] 
def update_q_table(state, action, reward, next_state):
  	# Get the old value of the current state-action pair
    old_value = ____
    # Determine the maximum Q-value for the next state
    next_max = ____
    # Compute the new value of the current state-action pair
    Q[state, action] = ____

alpha = 0.1
gamma = 0.95
Q = np.array([[10, 8], [20, 15]], dtype='float32')
# Update the Q-table
____
print(Q)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Reinforcement Learning dengan Gymnasium di Python

SkillTag.level.advancedSkillTag.label

4.8+

Mulai Kursus 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: Metode Monte Carlo Exercise 2: Pembuatan episode untuk metode Monte Carlo Exercise 3: Mengimplementasikan Monte Carlo kunjungan-pertama Exercise 4: Menerapkan Every-Visit Monte Carlo Exercise 5: Pembelajaran perbedaan temporal Exercise 6: Menerapkan aturan pembaruan SARSA Exercise 7: Menyelesaikan Frozen Lake 8x8 dengan SARSA Exercise 8: Q-learning Exercise 9: Menerapkan aturan pembaruan Q-learning

Latihan Saat Ini

Exercise 10: Menyelesaikan Frozen Lake 8x8 dengan Q-learning Exercise 11: Mengevaluasi kebijakan pada Frozen Lake yang licin

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!