rlmodule
Flexible reinforcement learning models instantiators library
Function approximators simple, but still strong. RNN - GRU - LSTM / SAC
Now it only supports skrl, but is intended to be library agnostic - in later expansion
try other algos shared separate model
How to run
Install rlmodule from local code
-
Make sure you are in base rlmodule dict.
-
Start virtual env.
python3 -m venv venv
source venv/bin/activate
- Install library from local code
pip install -e .
Note: sometimes installation may fail, if there is a run/ dir present, you may need to remove it (TODO: fix)
rm -rf runs
Deep policies: LayerNorm and Residual MLPs
rlmodule supports per-layer LayerNorm on MlpCfg and a SimBa-style residual
MLP (ResidualMlpCfg). These work for both separated and shared
architectures, with independent per-head granularity via the new
OutputLayerCfg.head field. See docs/layer_norm_and_residual.md
for motivation, references, and copy-pasteable cfg snippets.
Run chosen example
python3 rlmodule/skrl/torch/examples/gymnasium/pendulum_ppo_mlp_separate_model.py
Optional: observe run results in Tensorboard
tensorboard --logdir=runs/
Update new version to PIP
Change version name in pyproject.toml
pip install build twine
rm -rf runs
python -m build
twine upload dist/*
2 maintainers
Metadata
2 maintainers
Release files for rlmodule 0.2.0
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| rlmodule-0.2.0-py3-none-any.whl | Python 3 | none | any | Details |
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