Reinforcement Learning Maze Quiz

What is the primary goal of an agent in reinforcement learning?

• To minimize the number of steps

• To avoid all penalties

• To maximize its rewards

• To explore all possible paths

In Google Colab, how can you run a code cell?

• By dragging the cell

• By pressing Ctrl + Enter

• By double-clicking the cell

• By right-clicking the cell

What does a '1' represent in the maze layout?

• A wall

• The goal position

• An empty space

• The start position

What is the purpose of the reward system in reinforcement learning?

• To make the agent take random actions

• To guide the agent's behavior

• To increase the complexity of the maze

• To ensure the agent never hits a wall

What happens when an untrained agent navigates the maze?

• It takes random actions

• It receives high rewards

• It reaches the goal efficiently

• It avoids all walls

During training, what does the Q-table help the agent to do?

• Avoid all penalties

• Increase the number of steps

• Identify the shortest path

• Track the best moves to maximize rewards

What does the graph of rewards indicate during training?

• The agent's learning progress

• The number of walls in the maze

• The agent's speed

• The complexity of the maze

How can you experiment with different reward systems?

• By changing the maze layout

• By adjusting the reward and penalty values

• By increasing the number of episodes

• By using a different programming language

What is a potential risk of setting high rewards in reinforcement learning?

• Permissive behavior

• Slow learning

• Underfitting

• Overfitting

What is the final optional step in the tutorial?

• Adding more walls to the maze

• Increasing the number of episodes

• Changing the programming language

• Creating custom mazes