Dear researchers,
if you are looking for a research topic in reinforcement learning, I have something new for you.
We have just launched our new open source reinforcement learning environment. Here you can find it: https://github.com/dynamik1703/gym_longicontrol
Our new environment is in the field of autonomous driving. It offers the possibility to test and further develop algorithms for the efficient longitudinal control.
The longitudinal control problem has various challenges. One example is the trade-off between conflicting goals of travel time minimization and energy consumption. They contradict each other because a fast driving vehicle leads to high-energy consumption and vice versa.
Through the proposed RL environment, which is adapted to the OpenAi Gym standardization, we show that it is easy to prototype and implement state-of-art RL algorithms. Besides, the LongiControl environment is suitable for various examinations. In addition to the comparison of RL algorithms and the evaluation of safety algorithms, investigations in the area of Multi-Objective Reinforcement Learning are also possible. Further possible research objectives are the comparison with planning algorithms for known routes, investigation of the influence of model uncertainties and the consideration of very long-term objectives like arriving at a specific time.
LongiControl is designed to enable the community to leverage the latest strategies of reinforcement learning to address a real-world and high-impact problem in the field of autonomous driving.
Have fun trying it out! If you have any questions, feel free to write.
Dear Mohamed,
the LongiControl Environment is intended for the development and testing of Deep RL algorithms.
An execution of the learned agent in a real vehicle is not the goal.
The environment can be run on an ordinary laptop and does not require any special hardware.
What is your research goal?
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