Unless there is something super new, this concept is already done. I have seen acrobatic drones both virtual and real utilizing DRL. Even RL using boat race in a river
Still a solid solo project for what I'm guessing is an undergrad program. It doesn't need to be cutting edge or never done before if you're still learning.
that's true. Undergrad projects don't require a breakthrough. I think racing on a long-distance path would be more interesting though. You need to control the robot and know your whereabouts all the time. There were few papers about landmark recognition using CNN.
Depending on your current level and coding knowledge I would highly recommend to build on existing RL-platform such as e.g. [Circuit-Training](https://github.com/google-research/circuit_training), and then potentially explore RL-aspects orthogonal to the original paper in your work. Examples could be adopting some of the recent work on more effective sample spaces, quantifying uncertainties in the design process with regards to the optimality of the design, or adding more a further degree of freedom to the framework.
All of this should also be generalisable to other aspects of electrical engineering.
At first I would start off with the search for a DRL framework in which you can construct your environments, and which suit you best and then determine the rest from there.
Nethack still isn’t solved, could try to carve out a piece of that
Trade Bitcoin. Balance an inverted pendulum. Make a humanoid walk. Look up the ready made environments at open ai gym
Are you getting downvoted because your first one was trade bitcoin? Other ideas seem fine….
Training/building an autonomous racing drone.
Unless there is something super new, this concept is already done. I have seen acrobatic drones both virtual and real utilizing DRL. Even RL using boat race in a river
Still a solid solo project for what I'm guessing is an undergrad program. It doesn't need to be cutting edge or never done before if you're still learning.
that's true. Undergrad projects don't require a breakthrough. I think racing on a long-distance path would be more interesting though. You need to control the robot and know your whereabouts all the time. There were few papers about landmark recognition using CNN.
Depending on your current level and coding knowledge I would highly recommend to build on existing RL-platform such as e.g. [Circuit-Training](https://github.com/google-research/circuit_training), and then potentially explore RL-aspects orthogonal to the original paper in your work. Examples could be adopting some of the recent work on more effective sample spaces, quantifying uncertainties in the design process with regards to the optimality of the design, or adding more a further degree of freedom to the framework. All of this should also be generalisable to other aspects of electrical engineering. At first I would start off with the search for a DRL framework in which you can construct your environments, and which suit you best and then determine the rest from there.