===== T4b-inch - Deployment of Reinforcement Learning on a Real Robot ======


|**Deadline**| January 05, 2025, 23:59 PST |
|**Points**  |  5 (**Bonus Points**) |
|**Label in BRUTE**  | t4b-inch |
|**Files to submit** | archive with ''evaluator.py'' and ''agent.msh''|

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==== Introduction ====

This task extends the previous task; hence, students are advised to familiarize themself with the [[courses:uir:hw:t4a-rl|T4a-rl - Reinforcement Learning]] task first as all of the information from the previous task is valid for this task as well.

==== Assignement ====

This task further evaluates the trained gait by deploying the trained gait to a real inchworm robot or by further evaluation in the BRUTE system.

==== Evaluation ====

The project files (''evaluator.py'' and ''agent.msh'') are submitted to the BRUTE system before the student's exam, when the automatic evaluation assigns additional points.
The deployment will be possible during the last lab, when the experimental setup, including the required packages and hardware, is prepared for the student's convenience on designated PCs.

Before the deployment, the submitted ''agent.msh'' is downloaded from BRUTE to a designated PC.
The gait is run for 30 seconds on the real robot, starting in the most prolonged pose.
  * Each 5 cm traversed by the backmost part of the robot is awarded a single point. 
The median of three runs is used to produce the final value score.
A run where the robot loses stability or fails in another way can be repeated, up to three repetitions in total.

==== Hint and Observations ==== 

For a real deployment, two additional criteria should be considered.
Firstly, the proposed gait should be able to disengage the scales when moving forward and reengage them when staying in place.
Secondly, the proposed gait should not unnecessarily lift the centre of mass, as it makes a robot more prone to losing balance.