Quadcopter using Reinforcement Learning
My implementation of the DDPG reinforcement learning algorithm to solve the problem of a quadcopter taking flight.
I have included a reference to the DDPG paper used in the development of the flying agent:
Continuous control with deep reinforcement learning Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.
You can see the best take-off that was produced from my reward function and DDPG implementation below:
Although I am partial to this series of flight paths that occurred during the development phase of this project.
You can see my full analysis of my quadcopter in the notebook, but a snippet is included below
I went with take-off in the end, although I did try landing at first. I was having a lot of difficulty getting started with landing, so I switched over to take-off. I think one of the issues with my landing approach was that my reward function was a distance based one, which meant there was no attempt to setup an ‘approach’.
I went through a couple of iterations with my reward function, each time implementing a new factor. I split the function into the computation of two components: reward and penalty. I wanted to incentivize the agent into certain behaviours, while discouraging results that were not optimal. I describe it this way, because I wished to reflect these ideas:
- If you are stable, you have a lower penalty.
- If you are reckless (flying sideways), you have a higher penalty.
- The closer you are, the lower the penalty.
- The further you are, the higher the penalty.
- You are rewarded for proximity (cumulative)
- You are rewarded for still flying
These ideas didn’t translate well in the end, and in the end only a weighted distance was used for computing the reward. You can see that
reward=0 => abs(0-penalty) = penalty for the function. This means that most of the code in the get_reward function is not actually needed. The best decision was to ensure that the reward function was normalized, as that made the simulation a lot more consistent between runs.