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Line Following with Agent-Based Decision Making #147
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…en 0 and maxSpeed.
… into the rl_supervisor.
…s with sensor data transmission.
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Please increase number of PRs and keep logical changes small.
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For every python function that returns something, please try to use type-hinting for the return values on the function definition. This helps better follow and understand the code
CI Python requirements
{ | ||
/* Failed to send data to the supervisor. Go to error state. */ | ||
ErrorState::getInstance().setErrorMsg("DSF"); | ||
/* Failed to send Senssor data to the supervisor. Go to error state. */ |
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This works. But could be done better by using if-else blocks
…nto feature/RL Merged branch 'feature/RL' into 'feature/RL' to incorporate upstream updates and resolve conflicts
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Reinforcement training loop runs as expected. Further architecture changes needed to drastically reduce coupling between environment and RL Agent. This will be done in future updates regarding issue #156.
Implemented an agent for decision making in line following, added a trajectory buffer and networks, and created a plot file for analysis of the results.