RL-pybullets-cf

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RL-pybullets-cf

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Re-implementation of reinforcement learning based quadcopter control in gym-pybullet-drones.

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# Reinforcement Learning for Quadcopter Control

This repository is a fork of [gym-pybullet-drones](https://github.com/utiasDSL/gym-pybullet-drones) and implements a reinforcement learning based control policy inspired by [Penicka et al. [1]](https://rpg.ifi.uzh.ch/docs/RAL_IROS22_Penicka.pdf).

## Documentation

For documenation and a summary of the results, see our 4-pages [whitepaper](./media/pybullet_drones_whitepaper.pdf).

## Result

![RL Control Result](https://raw.githubusercontent.com/danielbinschmid/RL-pybullets-cf/main/media/rl_control.gif)

- The drone can follow arbitrary trajectories. 
- It is given two next target waypoints as observation. If the two target waypoints are close, it will reach the target slower. 
- The learned policy corresponds to the obtained result after _slow-phase training_ in [Penicka et al. [1]](https://rpg.ifi.uzh.ch/docs/RAL_IROS22_Penicka.pdf).

## Implemented New Features

- Reward implementation of RL policy proposed by [Penicka et al. [1]](https://rpg.ifi.uzh.ch/docs/RAL_IROS22_Penicka.pdf).
- Attitude control action type. In [gym-pybullet-drones](https://github.com/utiasDSL/gym-pybullet-drones), only motor-level control using PWM signals is implemented. This repository extends the original implementation and adds a wrapper for sending attitude commands (thrust and bodyrates).
- Random trajectory generation using polynomial minimum snap trajectory generation using [large_scale_traj_optimizer [2]](https://github.com/ZJU-FAST-Lab/large_scale_traj_optimizer) for training and test set generation. Implementation in [trajectories](./trajectories/) subfolder.
- Scripts for bechmarking the policy by computing basic benchmarks such as mean and max deviation from the target trajectory and time until completion.

## Setup

Tested on ArchLinux and Ubuntu. Note that Eigen must be installed on the system. On linux, install via your package manager. E.g. on Ubuntu:

```s
$ sudo apt-get install libeigen3-dev
```

It is strongly recommended to use a python virtual environment such as _conda_ or _pyvenv_.

1. Initialise repository. Repository must be pulled recursively

```s
$ git clone git@github.com:danielbinschmid/RL-pybullets-cf.git
$ git submodule --init --recursive
```

2. Initialise virtual environment. Tested with python version 3.10.13. E.g.:

```s
$ pyenv install 3.10.13
$ pyenv local 3.10.13
$ python3 -m venv ./venv
$ source ./venv/bin/activate
$ pip3 install --upgrade pip
```

3. Install dependencies and build

```s
$ pip3 install -e . # if needed, `sudo apt install build-essential` to install `gcc` and build `pybullet`
```

## Usage

Scripts for training, testing and visualization are provided.

### Training

To train the RL policy from scratch with our implementation, run

```s
$ cd runnables
$ ./train_rl.sh
```

It will produce a folder with the weights. Later, this weights folder can be passed to the visualization and testing scripts.

### Testing

To run our small benchmark suite, run

```s
$ cd runnables
$ ./test_rl.sh
$ ./test_pid.sh
```

Out of the box, it will use our pre-trained weights. Each bash script produces a .json file with the benchmarks.

### Visualization

To just visualize the control policy, run

```s
$ cd runnables
$ ./vis_rl.sh
```
Out of the box, it will use our pre-trained weights and randomly generated trajectories.

### Evaluation track generation

To generate a test set with random tracks, run

```s
$ cd runnables/utils
$ python gen_eval_tracks.py
```

### Plot generation

To generate the plots used in our whitepaper, run

```s
$ cd runnables
$ ./generate_plots.sh
```

## Dev

- Autoformatting with _black_.

### Test

Run all tests from the top folder with

```sh
pytest tests/
```

## Common Issues

- *Mismatching CMakeCache.txt in [trajectories/trajectories_generation](./trajectories/trajectories_generation).* Solution: Remove CMakeCache.txt in build folder of [trajectories/trajectories_generation](./trajectories/trajectories_generation).

## References

- [1]: Penicka, Robert, et al. [*Learning minimum-time flight in cluttered environments.*](https://rpg.ifi.uzh.ch/docs/RAL_IROS22_Penicka.pdf) IEEE Robotics and Automation Letters 7.3 (2022): 7209-7216.
- [2]: Burke, Declan, Airlie Chapman, and Iman Shames. [*Generating minimum-snap quadrotor trajectories really fast.*](https://ieeexplore.ieee.org/abstract/document/9341794) 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. [github](https://github.com/ZJU-FAST-Lab/large_scale_traj_optimizer)

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