Migration to RLStudio of basic robot mesh problem

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MIGRATION

The previous implementation (no RL-Studio related) is documented in this post This migration consisted of:

  • Migrating the learning algorithm to make the robot behave well in maze problem.
  • Adapt robot actions to step from one maze cell to the other on each step.
  • Adapt rewards as a non-stationary problem.
  • perform several tests to conclude that the goal is achieved as soon as posible.

you can find all the iterations tested in the results uploaded in the repository.

In there you will notice that there is not need to give plenty of information to the agent through the reward function (and indeed it could be counterproductive).

  • If you give a reward of 1 when the goal is achieved and 0 otherwise, the robot finally learn the optimal path.
  • If you give a reward of -1 when the robot crash and reset the scenario afterwards, does not matter what reward you give when reaching the goal. The robot will learn to avoid the walls and will never reach the goal.
  • If you stop the episode when the robot colide with a wall, the robot will probably never reach the goal and so, never learn.

The optimal reward configuration and hyperparameters can be found in the uploaded agent code In the same way, there you will find the worlds and models used.

Note also that two different environments were implemented and a robot has been build for each one. This is not a coincidence. Due to the algorithm used constraints, each step must lead to the same consecuent state. If a step in a status leads to different states in different iterations, that means that the problem is not following the premise stated by the markov rules to the qlearning algorithm implementation. That said, when the robot dimensions are not fitting closely the aisle dimensions, the robot could collide to the wall and the complete problem will change if the maze matrix is not matching the robot actions.

conclussion solution is not guaranteed when robot radius is not close to the aisle width.

However, to minimize this behavior, a parameter “reset_on_crash” has been enabled in the [configuration.yml]

RL-Studio related

Additionally, the following steps were accomplished to adapt the problem to RL-Studio:

  • Create a .yml configuration file
  • Including the algorithm in “algorithms” folder
  • Including the agent in “agents” folder
  • Push the model and world referenced in the configuration file to the CustomRobots repository
  • Adding the environment in /rl-studio/init file
  • Adding a folder in /rl-studio/envs (no major modifications with respect to the other implementations in rl-studio)

DEMO

Basic maze

Complex maze

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