diff --git a/README.md b/README.md index d38818d..3eb631b 100644 --- a/README.md +++ b/README.md @@ -19,9 +19,8 @@ Developed by: According to the "Capstone Projects" pdf, which introduced us to the possible final projects, the goal for the AWS DeepRacer Project is to "**train a 1/18th" scale car to race autonomously using Reinforcement Learning.**" ## Intro to Reinforcement Learning -Reinforcement Learning differs from the other 2 basic Machine Learning paradigms (Supervised & Unsupervised). A specific difference we can point to between Reinforcement Learning and Supervised Learning is the unecessary input/output labellings as RL algorithms typically use dynamic programming techniques with the goal of automatically improving through reward maximization. +Reinforcement Learning differs from the other 2 basic Machine Learning paradigms (Supervised & Unsupervised). A specific difference we can point to between Reinforcement Learning and Supervised Learning is the unnecessary input/output labelings as RL algorithms typically use dynamic programming techniques with the goal of automatically improving through reward maximization. -Reinforcement Learning differs from the other 2 basic Machine Learning paradigms (Supervised & Unsupervised). A specific difference we can point to between Reinforcement Learning and Supervised Learning is the unecessary input/output labellings as RL algorithms typically use dynamic programming techniques with the goal of automatically improving through reward maximization.

image @@ -239,4 +238,4 @@ The most valuable part of this was being able to get hands-on experience with Ap ### **Considerations and Future Goals** While the learning process is crucial, it's even more important to question the implications of what you're doing. This is especially true as the project you are working on is more applied/practical. We dove deeper into the history of driving and the possible biases and impacts of our project in the real world. An example of the considerations that should be taken into account is how our project and trained models wouldn't be able to be applied in many real-world scenarios as roads are very different depending on where you are. This can cause a disparity in access to this new technology. For more in-depth discussion on these questions feel free to read the `EthicalConsiderationsWksht.md` within this repo. -If we had more time in the future, some goals that we would love to accomplish are the optimization of our model to be faster than 10 seconds per lap as well as testing more on other tracks to avoid overfitting to one track. With regards to the physical environment, we'd hope to create a more permanent track using EVA foam pieces so that it is collapsable/transportable. Both virtually and physically, entering a competition is something that would allow us to compare and discuss our models and training environments to others who may have more experience or understanding in the field of Reinforcement Learning and Autonomous Driving. \ No newline at end of file +If we had more time in the future, some goals that we would love to accomplish are the optimization of our model to be faster than 10 seconds per lap as well as testing more on other tracks to avoid overfitting to one track. With regards to the physical environment, we'd hope to create a more permanent track using EVA foam pieces so that it is collapsable/transportable. Both virtually and physically, entering a competition is something that would allow us to compare and discuss our models and training environments to others who may have more experience or understanding in the field of Reinforcement Learning and Autonomous Driving.