CSC American University MountainCarContinuous v0 Discussion
Question Description
https://colab.research.google.com/drive/1uHWOfjNti…
In summary:
The code above implements an evaluate_agent() function that estimates performance of a given agent on an environment. random_agent is a completely random baseline. Random_nn gives us a randomly initialized neural network. random_policy_search tries n randomly initialized neural networks and keeps the best, plotting peak performance over time.
For this homework:
- Choose another environment from the list of classic control environments besides CartPole.a. What are its input (state space) and output (actions)?b. How does the random agent perform?c. How does random_policy_search perform?
- Choose a parameter of your choice to vary, and investigate how it impacts performance. For example, you could change num_hidden, or anything else that is an arbitrarily chosen parameter. Does it matter for performance, and what are its effects?
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