Deep Reinforcement Learning for Airsim Environment

Quadrotor Self-Flight using Depth image

NOTE

It is a capstone project for undergraduate course. It did work when I tried, but there were many trial and errors. I'm sorry that I didn't consider any reproducibility (e.g. random seed).

Check 1 min madness

Environment

Link to download executable

NOTE: These executables can be run only on Windows OS.

Easy Normal Hard

How To Use

Execute the environment first. If you can see the rendered simulation, then run what you want to try (e.g. python td3_per.py)

Description

Unreal Engine 4

• Original environment

1. Vertical column
2. Horizontal column
3. Window
4. Vertical curved wall

• Different Order of obstacles environment

1. Window
2. Horizontal column
3. Vertical curved wall
4. Vertical column

• Different type of obstacles environment

1. Horizontal curved wall
2. Reversed ㄷ shape
3. ㄷ shape
4. Diagonal column

Parameter

• Timescale: 0.5 (Unit time for each step)
• Clockspeed: 1.0 (Default)
• Goals: [7, 17, 27.5, 45, 57]
• Start position: (0, 0, 1.2)

Reset

Respawn at the start position, and then take off and hover.
It takes about 1 sec.

Step

Given action as 3 real value, process moveByVelocity() for 0.5 sec.
For delay caused by computing network, pause Simulation after 0.5 sec.

Done

If a collision occurs, including landing, it would be dead. If x coordinate value is smaller than -0.5, it would be dead. If it gets to the final goal, the episode would be done.

State

• Depth images from front camera (144 * 256 or 72 * 128)
• (Optional) Linear velocity of quadrotor (x, y, z)

Action

• Discrete Action Space (Action size = 7)
  Using interpret_action(), choose +/-1 along one axis among x, y, z or hovering.

• Continuous Action Space (Actions size = 3)
  3 real values for each axis. I decided the scale as 1.5 and gave a bonus for y axis +0.5.

Reward

• Dead: -2.0
• Goal: 2.0 * (1 + level / # of total levels)
• Too slow(Speed < 0.2): -0.05
• Otherwise: 0.1 * linear velocity along y axis

(e.g. The faster go forward, The more reward is given. The faster go backward, The more penalty is given.)

Agent

• Recurrent DQN
• Recurrent A2C
• Recurrent DDPG
• Recurrent DDPG + PER
• Recurrent TD3 + PER (BEST)

Result