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Scenario We are on the way to setting foot on Mars. To do so , we need to refuel our space capsule in a space

Scenario
We are on the way to setting foot on Mars. To do so, we need to refuel our space capsule in a space station thousands of miles away from Earth.
We need to perform a docking operation similarly as shown in this video.
Goal
Build a reinforcement learning model to perfom the docking operation autonomously while respecting safety and time constraints.
Time constraint
The docking operation must be done in 5 minutes (300 s)
Safety constraint
For the docking operation to be performed safely, the space capsule needs to be aligned +/-15 cm from the docking center of the space station.
Actions
You only have 3 actions: go left, do nothing, go right.
Observations
Space capsule alignment with the space station. It is assumed the alignment can range from -2 m to 2 m, where 0 m means perfect alignment. We also assume that negative values describe a misalignment to the left and positive values represent a misalignment to the right.
Tasks
Create a new .py file called SpaceCapsuleEnvironment.py and build a model that keeps the space capsule in the safety range for as long as possible using SARSA algorithm.
1)[15 points] Create a SpaceEnv() class with the following function
[5 points]__init__(self), an initilization function that defines the initial values for the environment.
[5 points] step(self, action), a step function that provides the next observation (state) of the system, a reward, and stopping criteria (done) given an action.
[5 points] reset(self), a function to reset the values of the environment after each epsiode.
2)[5 points] Discretize the alignment range in 100 evenly spaced numbers over the interval -200 cm and 200 cm. You should use the function np.linspace. Also, update the function getState(observation).
3)[10 points] Run SARSA algorithm with the SpaceEnv() you've just created. You should save the plot containing the results.
4)[10 points] Change the learning rate parameter ALPHA to 0.2 and then to 0.5, and discuss your findings.

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