Menghitung Nilai Q

Tujuan Anda adalah menghitung nilai aksi, atau Q-value, untuk setiap pasangan state-action pada lingkungan kustom MyGridWorld saat mengikuti kebijakan berikut. Dalam RL, Q-value penting karena merepresentasikan utilitas yang diharapkan dari mengeksekusi suatu aksi tertentu pada state tertentu, kemudian mengikuti kebijakan tersebut.

Lingkungan telah diimpor sebagai env bersama fungsi compute_state_value() dan variabel yang diperlukan (terminal_state, num_states, num_actions, policy, gamma).

Latihan ini adalah bagian dari kursus

Reinforcement Learning dengan Gymnasium di Python

Petunjuk latihan

Lengkapi fungsi compute_q_value() untuk menghitung nilai aksi untuk state dan action tertentu.
Buat dictionary Q di mana setiap key merepresentasikan pasangan state-action, dan value yang sesuai adalah Q-value untuk pasangan tersebut.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Complete the function to compute the action-value for a state-action pair
def compute_q_value(state, action):
    if state == terminal_state:
        return None   
    probability, next_state, reward, done = ____
    return ____

# Compute Q-values for each state-action pair
Q = {(____, ____): _____ for ____ in range(____) for ____ in range(____)}

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: Proses Keputusan Markov Exercise 2: Komponen MDP Frozen Lake Kustom Exercise 3: Mengeksplorasi ruang state dan aksi Exercise 4: Probabilitas transisi dan reward Exercise 5: Kebijakan dan fungsi nilai-keadaan Exercise 6: Mendefinisikan kebijakan deterministik Exercise 7: Menghitung nilai-keadaan untuk sebuah kebijakan Exercise 8: Membandingkan kebijakan Exercise 9: Fungsi nilai-aksi Exercise 10: Menghitung Nilai Q

Latihan Saat Ini

Exercise 11: Meningkatkan sebuah kebijakan Exercise 12: Iterasi kebijakan dan iterasi nilai Exercise 13: Menerapkan policy iteration untuk kebijakan optimal Exercise 14: Mengimplementasikan 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!