[Docs] Should manually-defined agent behavior be allowed on the leaderboards?

[trevormcinroe]

There are a few solutions on the leaderboard with 0 episodes before solve.

Observing the code in these solutions shows that the agent behavior is simply a static, manually-defined policy. For example:

class Agent:
   def decide(self, observation):
       position, velocity, angle, angle_velocity = observation
       action = int(3. * angle + angle_velocity > 0.)
       return action

This algorithm is obviously not using any tenets of reinforcement learning, or even machine learning in general.
Even if this policy was jotted down from an actual RL-learned policy, it is not genuine to say that it was learned in 0 episodes.

The leaderboard itself is for fun, yes. But you could also argue that it is a place for RL beginners to read through code and learn.

This leaderboard is community-driven, so I ask the community: should solutions like this be allowed to occupy the top spot on the leaderboard?

[jkterry1]

It should be allowed. It's instructive for people to see useful things.