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 average reward achieved after running the DQN model?

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What advice was given regarding changing algorithms or policies?

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