97 lines
2.9 KiB
Markdown
97 lines
2.9 KiB
Markdown
# Learning Invariant Representations for Reinforcement Learning without Reconstruction
|
|
|
|
## Requirements
|
|
We assume you have access to a gpu that can run CUDA 9.2. Then, the simplest way to install all required dependencies is to create an anaconda environment by running:
|
|
```
|
|
conda env create -f conda_env.yml
|
|
```
|
|
After the installation ends you can activate your environment with:
|
|
```
|
|
source activate dbc
|
|
```
|
|
|
|
## Instructions
|
|
To train a DBC agent on the `cheetah run` task from image-based observations run:
|
|
```
|
|
python train.py \
|
|
--domain_name cheetah \
|
|
--task_name run \
|
|
--encoder_type pixel \
|
|
--decoder_type identity \
|
|
--action_repeat 4 \
|
|
--save_video \
|
|
--save_tb \
|
|
--work_dir ./log \
|
|
--seed 1
|
|
```
|
|
This will produce 'log' folder, where all the outputs are going to be stored including train/eval logs, tensorboard blobs, and evaluation episode videos. One can attacha tensorboard to monitor training by running:
|
|
```
|
|
tensorboard --logdir log
|
|
```
|
|
and opening up tensorboad in your browser.
|
|
|
|
The console output is also available in a form:
|
|
```
|
|
| train | E: 1 | S: 1000 | D: 0.8 s | R: 0.0000 | BR: 0.0000 | ALOSS: 0.0000 | CLOSS: 0.0000 | RLOSS: 0.0000
|
|
```
|
|
a training entry decodes as:
|
|
```
|
|
train - training episode
|
|
E - total number of episodes
|
|
S - total number of environment steps
|
|
D - duration in seconds to train 1 episode
|
|
R - episode reward
|
|
BR - average reward of sampled batch
|
|
ALOSS - average loss of actor
|
|
CLOSS - average loss of critic
|
|
RLOSS - average reconstruction loss (only if it is trained from pixels and decoder)
|
|
```
|
|
while an evaluation entry:
|
|
```
|
|
| eval | S: 0 | ER: 21.1676
|
|
```
|
|
which just tells the expected reward `ER` evaluating current policy after `S` steps. Note that `ER` is average evaluation performance over `num_eval_episodes` episodes (usually 10).
|
|
|
|
## CARLA
|
|
Download CARLA from https://github.com/carla-simulator/carla/releases, e.g.:
|
|
1. https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/CARLA_0.9.8.tar.gz
|
|
2. https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/AdditionalMaps_0.9.8.tar.gz
|
|
|
|
Add to your python path:
|
|
```
|
|
export PYTHONPATH=$PYTHONPATH:/home/rmcallister/code/bisim_metric/CARLA_0.9.8/PythonAPI
|
|
export PYTHONPATH=$PYTHONPATH:/home/rmcallister/code/bisim_metric/CARLA_0.9.8/PythonAPI/carla
|
|
export PYTHONPATH=$PYTHONPATH:/home/rmcallister/code/bisim_metric/CARLA_0.9.8/PythonAPI/carla/dist/carla-0.9.8-py3.5-linux-x86_64.egg
|
|
```
|
|
and merge the directories.
|
|
|
|
Then pull altered carla branch files:
|
|
```
|
|
git fetch
|
|
git checkout carla
|
|
```
|
|
|
|
Install:
|
|
```
|
|
pip install pygame
|
|
pip install networkx
|
|
```
|
|
|
|
Terminal 1:
|
|
```
|
|
cd CARLA_0.9.6
|
|
bash CarlaUE4.sh -fps 20
|
|
```
|
|
|
|
Terminal 2:
|
|
```
|
|
cd CARLA_0.9.6
|
|
# can run expert autopilot (uses privileged game-state information):
|
|
python PythonAPI/carla/agents/navigation/carla_env.py
|
|
# or can run bisim:
|
|
./run_local_carla096.sh --agent bisim --transition_model_type probabilistic --domain_name carla
|
|
```
|
|
|
|
## License
|
|
This project is CC-BY-NC 4.0 licensed, as found in the LICENSE file.
|