Model-Based Reinforcement Learning for Quadrotor
Implementation of Quadrotor Model-based Learning in pytorch and VREP Simulator based of the following papers:
Low-Level control of a quadrotor with deep model-based reinforcement learning
Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Current Progress
We are testing separately Fault-Free Case, and Fault-Motor 1 case for same trajectory.
Circle Trajectory
Here, we show trajectory followed by quadrotor in a Circle trajectory
Fault-Free Case, trajectory over time
Fault-M1 Case, trajectory over time
Same Comparison in 3D dim, Left Fault free, Right Fault Motor 1
Point Trajectory
Fault-Free Case, trajectory over time
Fault-M1 Case, trajectory over time
Same Comparison in 3D dim, Left Fault free, Right Fault Motor 1
Gif view
In the following gif we show both in Point Trajectory, Left: Fault-Free Case, Right: Fault-M1 Case
Left Fault-free (Blue), Fault case (Yellow) in Helicoid & Vertical-Sin paths
