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chore(rl): move rl related code to its directory at top level (#2002)

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* chore(rl): move rl related code to its directory at top level

* chore(style): apply pre-commit to renamed headers

* test(rl): fix rl imports

* docs(rl): update rl headers doc
This commit is contained in:
Steven Palma
2025-09-23 16:32:34 +02:00
committed by GitHub
parent 9d0cf64da6
commit d6a32e9742
12 changed files with 44 additions and 41 deletions
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8
docs/source/hilserl_sim.mdx
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@@ -91,7 +91,7 @@ Important parameters:
To run the environment, set mode to null:
```bash
python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
python -m lerobot.rl.gym_manipulator --config_path path/to/gym_hil_env.json
```
### Recording a Dataset
@@ -118,7 +118,7 @@ To collect a dataset, set the mode to `record` whilst defining the repo_id and n
```
```bash
python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
python -m lerobot.rl.gym_manipulator --config_path path/to/gym_hil_env.json
```
### Training a Policy
@@ -126,13 +126,13 @@ python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.j
To train a policy, checkout the configuration example available [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/rl/gym_hil/train_config.json) and run the actor and learner servers:
```bash
python -m lerobot.scripts.rl.actor --config_path path/to/train_gym_hil_env.json
python -m lerobot.rl.actor --config_path path/to/train_gym_hil_env.json
```
In a different terminal, run the learner server:
```bash
python -m lerobot.scripts.rl.learner --config_path path/to/train_gym_hil_env.json
python -m lerobot.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.