uav dqn gazebo .pdf

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Video results : FPV of quadrotor flight

Shut up and show me the code

Autonomous Quadrotor Flight in Simulation using RL
Ratnesh Madaan, Dhruv Mauria Saxena, Rogério Bonatti, Shohin Mukherjee

Motivation

Objectives

➢ Despite advancements in sensing technologies, it
is difficult to develop robust systems by separating
perception and control.
➢ Learning to fly in the real world is impractical since
it is time consuming and expensive.
➢ Learning to fly in simulation opens the possibility
of transferring learned policies to the real world.

➢ Develop an open-source Gazebo environment
integrated with Gym which can be used by the
community for reinforcement learning research.
➢ Train a deep Q-network capable of flying a drone
autonomously in the Gazebo environment.

Environment
➢ Environment consists of randomly places
cylindrical obstacles, simulated and rendered in
Gazebo.
➢ Position of cylinders changes for each episode.
➢ Quadrotor is equipped with a planar laser
rangefinder and a front-facing RGB-D camera.

Partial Results
Graphs for grayscale, monocular camera images
Train Reward

Learning On Images
Network architecture:
➢ Input: 84 X 84 X 4 images
➢ Conv layer 1: 32, 8 X 8 filters, stride 4
➢ Conv layer 2: 64, 4 X 4 filters, stride 2
➢ Conv layer 3: 64, 3 X 3 filters, stride 1
➢ Fully-connected layer: 512 units
➢ Output: 9 units (correspond to yaw angles)

Train Episode Length

Test Reward

Conclusion And Future Work
Learning On Laser Data
Network architecture:
➢ Input: 70 X 4 array
➢ Fully-connected layer: 512 units
➢ Fully-connected layer: 512 units
➢ Output: 9 units (number of actions)

Learning Algorithm - DQN
➢ Qw- : Target Network
➢ Qw : Online Network

➢ With less than 1M iterations, we still could not
observe significant learning in the environments
with laser and depth images as inputs.
➢ Learning on depth images and laser data can be
more easily transferrable to real-life
applications.
➢ During the summer we plan to test real
quadcopters flying with the policies learned in
simulation.

References
➢ [1] Mnih, Volodymyr, et al. "Playing atari with deep
reinforcement learning." arXiv preprint arXiv:1312.5602
(2013).
➢ [2] F. Sadeghi and S. Levine, “(cad) 2 rl: Real single-image flight
without a single real image,” arXiv preprint arXiv:1611.04201,
2016


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