Question

Modify the Values for the Exploration Factor, Discount Factor, and Learning Rates in the Code to Understand How Those Values Affect the Performance of the Algorithm. Be Sure to Place Each Experiment in a Different Code Block So That Your Instructor Can View All of Your Changes. A. # Set exploration factor to 0.2

exploration_factor = 0.2

# Rest of the code... B. # Set discount factor to 0.9
discount_factor = 0.9

# Rest of the code... C. # Set learning rate to 0.1
learning_rate = 0.1

# Rest of the code...

Answer 1

Exploration factor, discount factor, and learning rate are important parameters in reinforcement learning algorithms, specifically in Q-Learning.

Step-by-step explanation:

Exploration factor, discount factor, and learning rate are important parameters in reinforcement learning algorithms, specifically in Q-Learning. Let's explore how changing these parameters can affect the performance of the algorithm:

1. Exploration Factor (epsilon): This determines the balance between exploration and exploitation. A higher value (e.g., 0.2) encourages more exploration, resulting in the agent trying out different actions to discover optimal rewards. On the other hand, a lower value (e.g., 0) relies more on exploiting the current knowledge, potentially leading to suboptimal solutions.
2. Discount Factor (gamma): This determines the importance of future rewards. A higher value (e.g., 0.9) emphasizes long-term rewards, encouraging the agent to make decisions that maximize cumulative rewards. Conversely, a lower value (e.g., 0) makes the agent focus more on immediate rewards only, leading to short-sighted decisions.
3. Learning Rate (alpha): This controls the weight given to new information versus existing knowledge. A higher value (e.g., 0.1) means the agent quickly adapts to new experiences, potentially leading to instability. A lower value (e.g., 0) indicates the agent relies heavily on its existing knowledge and is less open to updating its strategies based on new information.

By modifying these parameters, you can observe how the agent's behavior changes, whether it explores more or exploits its knowledge, prioritizes short-term or long-term rewards, or quickly adapts or relies on past experiences.