Deep RL Quadcopter Controller

Training a quadcopter to take off using Actor-Critic approach based DDPG algorithm.

Table of Contents

• Quadcopter Environment
• Setup
  • System Configuration
  • Environment Setup
  • Instructions for getting started
  • Project Structure
• Algorithm Details
• Agent Performance
• Bibliography

Quadcopter Environment

• Observation Space: size = 12
  • x,y,z coordinates: size = 3
  • Euler Angles (phi.theta,psi): size = 3
  • Velocities along the 3 axis (v_x,v_y,v_z): size = 3
  • Angular Velocities (v_phi,v_theta,v_psi): size = 3
• Control Modes: 2
  • Single rotor mode: all rotor speeds are equal
  • Multi rotor mode: all rotors are independent
• Action Space
  • Size = 1 for single rotor mode
  • Size = 4 for multi rotor mode
• Boundary: 300 x 300 x 300 m
  • X: -150 --> 150 m
  • Y: -150 --> 150 m
  • Z: 0 --> 300 m
• Boundary Conditions
  • Hitting any of the environment boundaries leads to immediate termination of the episode
  • In addition, the sim.in_bounds flag is set to False, which can be used to assign any additional penalty for such a behaviour

Setup

System Configuration

The project was built with the following configuration:

• Ubuntu 16.04
• CUDA 10.0
• CUDNN 7.4
• Python 3.6
• Pytorch 1.0

Though not tested, the project can still be expected to work out of the box for most reasonably deviant configurations.

Environment Setup

• Create separate virtual environment for the project using the provided environment.yml file

conda env create -f environment.yml
conda activate quad

Instructions for getting started!

1. Clone the repository (if you haven't already!)

git clone https://github.com/1jsingh/RL_quadcopter.git
cd RL_quadcopter

2. Follow along with either Quadcopter-single_rotor.ipynb or Quadcopter-multi_rotor.ipynb to train your own RL agent.

Project Structure

• task.py: quadcopter agent reward formulation
• agents/:
  • model.py: actor and critic models
  • ddpg_agent.py: combined ddpg agent with Replay buffer and OU Noise
  • ddpg_agent_per.py: ddpg agent with prioritised experience replay
  • SumTree.py: sumtree implementation for per
  • bst.py: fixed size binary search tree for per
• physics_sim.py: simulator for the quadcopter. DO NOT MODIFY THIS FILE.
• Quadcopter-single_rotor.ipynb : notebook for training quad in single rotor control mode
• Quadcopter-multi_rotor.ipynb : notebook for training quad in multi rotor control mode

Algorithm Details

• The quadcopter agent is trained using Actor-Critic based DDPG algorithm.

• The Replay buffer is implemented using optimized prioritised experience replay.

Optimized implementation of prioritised experience replay

The concept of using prioritised experience replay is to sample experiences with higher TD errors with a higher probability.

However, doing so comes at the cost of higher sampling and update times to the experience buffer {D}.

The following shows the time complexity for key operations required for PER:

• Compute max priority from the experience replay: O(n)
• Compute sum of priorities for all samples from the experience replay: O(n)
• Insertion of new samples in the experience replay: O(1)

Thus time complexity for a naive implementation for PER : O(n)

In order to work around this problem, the implementation uses:

• SumTree data structure for O(1) computation of sum of priorities
• Fixed size binary search tree implementation for computing max priority in O(log(n)) time

Time complexity for fixed size binary search tree based optimized implementation of PER:

• Compute max priority from the experience replay: O(log(n))
• Compute sum of priorities for all samples from the experience replay: O(1)
• Insertion of new samples in the experience replay: O(1)

Thus the overall time complexity for optimized implementation of PER: O(log(n))

Agent Performance

Single rotor mode

• Reward function

  

• Reward Curve
  

• Quadcopter movement visualization
  

Multi rotor mode

• Reward function
  Due to the reduced stability in multi rotor mode, adding a constant positive reward of +1 at each time step, helps the agent to learn to stay afloat for a longer time duration

  

• Reward Curve
  

• Quadcopter movement visualization
  

  Note that the multi-rotor quadcopter agent does not perform as well as the single rotor agent

Bibliography

1. Lillicrap, Timothy P., et al. "Continuous control with deep reinforcement learning." arXiv preprint arXiv:1509.02971 (2015).
2. Schaul, Tom, et al. "Prioritized experience replay." arXiv preprint arXiv:1511.05952 (2015).
3. SumTree implementation: https://github.com/rlcode/per/blob/master/SumTree.py