CURLA: CURL x CARLA
Robust end-to-end Autonomous Driving by combining Contrastive Learning and Reinforcement Learning. This repository applies the CURL methodology to an end-to-end autonomous driving task in a custom simulated environment built on top of the CARLA simulator. The code allows users to train and evaluate visual RL agents using the Soft Actor-Critic (SAC) algorithm, combined with an auxiliary self-supervised contrastive learning task (instance discrimination) for better representation learning, leading to increased sample-efficiency and robustness/generalizability.
- CURL: Contrastive Unsupervised Representations for Reinforcement Learning (Laskin et al., 2020) [Paper/Code].
- CARLA: Open-source simulator for autonomous driving research (Dosovitskiy et al., 2017) [Paper/Code].
System requirements:
- Linux or Windows operating system
- An NVIDIA GPU with at least 6GB of memory
- At least 30GB of free disk space
- Anaconda or Miniconda
Recommended directory structure at the end of the installation:
.
├── my_project
│ ├── curla
│ ├── carla
Make the root directory and clone the curla repository:
mkdir my_project
cd path/to/my_project
git clone git@github.com:paulvantieghem/curla.git
All of the dependencies are in the environment.yml file. To create the conda environment, run:
cd path/to/my_project/curla
conda env create -f environment.yml
The recommened version of the CARLA simulator is version 0.9.8, because it is the most recent version proven to be stable enough not to crash during long experiments (~10^6 training steps). More recent versions of the CARLA simulator are compatible (versions 0.9.11 and 0.9.14 were also tested) with the code, but the CARLA simulator is prone to crashing during long training experiments.
Download the CARLA 0.9.8 release and extract it in the carla directory:
cd path/to/my_project
mkdir carla
cd carla
wget https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/CARLA_0.9.8.tar.gz
tar -xzf CARLA_0.9.8.tar.gz
rm CARLA_0.9.8.tar.gz
On Windows, you will have to download and extract the CARLA 0.9.8 release manually. Releases can be downloaded from the CARLA repository.
Install the CARLA client library using conda develop:
conda activate curla
conda install -y conda-build
conda develop path/to/my_project/carla/PythonAPI/carla/dist/carla-0.9.8-py3.5-linux-x86_64.egg
conda deactivate
To train a CURL agent for N training steps on the highway of Town04 with the default (hyper)parameters, run the command listed below. Note that the train.py script will automatically launch the CARLA simulator, so no need to launch one manually.
conda activate curla
cd path/to/my_project/curla
python train.py --num_train_steps N
In your console, you should see printouts that look like:
| train | E: 801 | S: 274981 | ER: 344.330 | BR: 0.2396 | A_LOSS: -14.4758 | CR_LOSS: 17.1911 | CU_LOSS: 0.0025
| train | E: 802 | S: 275000 | ER: -6.7408 | BR: 0.0000 | A_LOSS: 0.000000 | CR_LOSS: 0.00000 | CU_LOSS: 0.0000
| eval | S: 275000 | MER: 104.6457 | BER: 526.8412
| train | E: 803 | S: 276000 | ER: 469.893 | BR: 0.2631 | A_LOSS: -14.9994 | CR_LOSS: 5.94400 | CU_LOSS: 0.0068
| train | E: 804 | S: 277000 | ER: 536.399 | BR: 0.2714 | A_LOSS: -14.4183 | CR_LOSS: 5.31420 | CU_LOSS: 0.0028
Log abbreviation mapping:
train - training episode
E - total number of episodes
S - total number of environment steps
ER - episode reward
BR - average reward of sampled batch
A_LOSS - average loss of actor
CR_LOSS - average loss of critic
CU_LOSS - average loss of the CURL encoder
eval - evaluation episode
S - total number of environment steps
MER - mean evaluation episode reward
BER - best evaluation episode reward
All data related to the run is stored in the specified by --working_dir_name (default: experiments). To enable model or video saving, use the --save_model or --save_video flags. For all available flags, inspect train.py.
Tensorboard logging is enabled by default. To view the logs, run the following command in the curla directory:
tensorboard --logdir experiments --port 6006
For custom training, have a look at the section 4. Custom Training
At each evaluation step during training, the current CURL model is saved in the experiments/experiment_name/model directory. To evaluate a saved model, run:
eval.py --experiment_dir_path path/to/experiment_name --model_step 1_000_000
Here, experiment_dir_path is the path to the directory of the experiment you want to evaluate that contains the model directory, and model_step is the training step at which the model was saved. The rest of the arguments are automatically read from the args.log file in the experiment directory to match the training arguments exactly.
Possible methods to train agents:
- Pixel SAC:
python train.py --pixel_sac - CURL with the identity augmentation:
python train.py --augmentation identity - CURL with the random crop augmentation:
python train.py --augmentation random_crop - CURL with the color jiggle augmentation:
python train.py --augmentation color_jiggle - CURL with the noisy cover augmentation:
python train.py --augmentation noisy_cover
For hyperparameter tweaking and simulator settings, have a look at the arguments that can be passed to train.py and the configuration in settings.py. Custom augmentations can be added to augmentations.py
Visualization of the augmentations:
color_jiggle_example.mp4
Note: The oscillations due to sudden steering commands are partly due to the physics in CARLA version 0.9.8. More recent versions (>=0.9.10) have updated turn physics, making the results much smoother. These newer versions of CARLA are not reliable/stable enough for long training however, as discussed earlier. Another option is add a moving average to the steering command, but this comes at the risk of slower reactions.
