Alter the Reward function used in learn.py about gym-pybullet-drones

[JacopoPan]

@ArminBaz the reward function in the latest commit is not the same of when I wrote the message above.
Have you tried looking at the performance of the trained agent using script test_singleagent.py?
It should be under folder gym-pybullet-drones/experiments/learning/results

$ python ./test_singleagent.py --exp ./results/save-<env>-<algo>-<obs>-<act>-<time-date>

(-30 over the episode should be ok, as there are negative rewards for any point except the desired hover one)

[ArminBaz]

@JacopoPan That makes a lot of sense, thank you for getting back so quickly!

[JacopoPan]

Hello @amijeet,
apologies if I break workflows (especially around learn.py) as I am actively modifying the code.
If you want to get started on single agent RL, look at this commit and, in particular,

These 2 scripts:

• singleagent.py—using a few of stable-baselines3 algorithms
• test_singleagent.py—to re-run a model trained with the previous script

And these 2 classes:

• BaseSingleAgentAviary.py
• TakeoffAviary.py

This is a much simplified take-off and hover scenario with a 2-D obs space (z and velocity in z) and a 1-D action space (the RPM for all motors).

The reward is 1 for z between 0.75 and 0.99 and 0 otherwise.

In this example, running stable-baselines3's PPO finds a solution in just a few minutes.

$ cd gym-pybullet-drones/experiments/learning/
$ python singleagent.py --env takeoff --algo ppo --pol mlp --input rpm

Output:

Eval num_timesteps=10000, episode_reward=26.00 +/- 0.00
Episode length: 242.00 +/- 0.00
New best mean reward!
Eval num_timesteps=20000, episode_reward=29.00 +/- 0.00
Episode length: 242.00 +/- 0.00
New best mean reward!
Eval num_timesteps=30000, episode_reward=58.00 +/- 0.00
Episode length: 242.00 +/- 0.00
New best mean reward!
Eval num_timesteps=40000, episode_reward=173.00 +/- 0.00
Episode length: 242.00 +/- 0.00
New best mean reward!
Stopping training because the mean reward 173.00  is above the threshold 100

Of course, more complicated tasks, using higher dimensional observations and action vectors can require:

• More sophisticated reward engineering (see TakeoffAviary.py)
• And/or to customize the learning networks architecture (see singleagent.py)

as well as much longer training times. E.g. simply making the input 4-D complicates the problem enough that PPO only collects 1/5 of the reward in 15x the number of iterations:

Eval num_timesteps=680000, episode_reward=31.00 +/- 0.00
Episode length: 86.00 +/- 0.00
New best mean reward!

I don't have all the answers, the purpose of this gym is exactly to try (and let others try) these things.

[ArminBaz]

Hey @JacopoPan, forgive me if this is a naive question as I am still relatively new to reinforcement learning and your library. I just ran singleagent.py (from the most recent commit) on takeoff and I noticed that my model seems to be far slower than the one you showed.

It seems that you were able to break the mean reward threshold after 40000 timesteps. While I am stuck in -30 at around 120000. Do you know why this may be happening and do you have any suggestions on how to speed up the training? Thanks!

Here is the output for reference:

Eval num_timesteps=110000, episode_reward=-30.23 +/- 0.00
Episode length: 242.00 +/- 0.00
Eval num_timesteps=115000, episode_reward=-30.18 +/- 0.00
Episode length: 242.00 +/- 0.00
New best mean reward!
Eval num_timesteps=120000, episode_reward=-30.15 +/- 0.00
Episode length: 242.00 +/- 0.00
New best mean reward!
Eval num_timesteps=125000, episode_reward=-30.12 +/- 0.00
Episode length: 242.00 +/- 0.00
New best mean reward!