Reinforcement Learning and Deep RL Python Theory and Projects - Changing the Algorithm

The video tutorial explores changing an algorithm from PPO to DQN, despite DQN not being a good fit for the problem. It guides through importing and setting up DQN, training the model for 20,000 steps, and evaluating its performance. The results show poor performance, highlighting the importance of choosing the right algorithm. The tutorial concludes by emphasizing the ease of changing algorithms and policies.

What was the initial algorithm used before switching to DQN?

• A3C

• PPO

• REINFORCE

• DQN

Why was DQN considered a poor fit for the problem?

• It requires more computational power

• It is too complex to implement

• It is not compatible with the software used

• It doesn't handle continuous action spaces well

What is the purpose of using a callback during model training?

• To visualize the training process

• To adjust hyperparameters automatically

• To monitor and evaluate the model's performance

• To save the model at regular intervals

How many time steps were used to train the DQN model?

• 10,000

• 15,000

• 20,000

• 25,000

What was the average reward obtained by the DQN model?

• 5

• 15

• 20

• 10