Package folder structure (#1417)

* Move files

* Replace imports & paths

* Update relative paths

* Update doc symlinks

* Update instructions paths

* Fix imports

* Update grpc files

* Update more instructions

* Downgrade grpc-tools

* Update manifest

* Update more paths

* Update config paths

* Update CI paths

* Update bandit exclusions

* Remove walkthrough section
This commit is contained in:
Simon Alibert
2025-07-01 16:34:46 +02:00
committed by GitHub
parent 483be9aac2
commit d4ee470b00
268 changed files with 862 additions and 890 deletions

View File

@@ -77,7 +77,7 @@ Important parameters:
To run the environment, set mode to null:
```python
python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json
python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
```
### Recording a Dataset
@@ -85,7 +85,7 @@ python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.j
To collect a dataset, set the mode to `record` whilst defining the repo_id and number of episodes to record:
```python
python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json
python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
```
### Training a Policy
@@ -93,13 +93,13 @@ python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.j
To train a policy, checkout the configuration example available [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/train_gym_hil_env.json) and run the actor and learner servers:
```python
python lerobot/scripts/rl/actor.py --config_path path/to/train_gym_hil_env.json
python -m lerobot.scripts.rl.actor --config_path path/to/train_gym_hil_env.json
```
In a different terminal, run the learner server:
```python
python lerobot/scripts/rl/learner.py --config_path path/to/train_gym_hil_env.json
python -m lerobot.scripts.rl.learner --config_path path/to/train_gym_hil_env.json
```
The simulation environment provides a safe and repeatable way to develop and test your Human-In-the-Loop reinforcement learning components before deploying to real robots.