metastable-baselines2/README.md

# Metastable Baselines 2

<p align='center'>
  <img src='./icon.svg'>
</p>

An extension to Stable Baselines 3. Based on Metastable Baselines 1.

This repo provides:

- An implementation of ["Differentiable Trust Region Layers for Deep Reinforcement Learning" by Fabian Otto et al. (TRPL)](https://arxiv.org/abs/2101.09207)
- Support for Contextual Covariances
- Support for Full Covariances

## Installation

#### Install dependency: Metastable Projections

Follow instructions for the [Metastable Projections](https://git.dominik-roth.eu/dodox/metastable-projections) ([GitHub Mirror](https://github.com/D-o-d-o-x/metastable-projections)).
KL Projections require ALR's ITPAL as an additional dependecy.

#### Install as a package

Then install this repo as a package:

```bash
pip install -e .
```

If you want to be able to use full / contextual covariances, install with the optional dependency 'pca':
```bash
pip install -e '.[pca]'
```
## Usage

### TRPL

TRPL can be used just like SB3's PPO:

```python
import gymnasium as gym
from metastable_baselines2 import TRPL

env_id = 'LunarLanderContinuous-v2'
projection = 'Wasserstein' # or Frobenius or KL

model = TRPL("MlpPolicy", env_id, n_steps=128, seed=0, policy_kwargs=dict(net_arch=[16]), projection_class=projection, verbose=1)

model.learn(total_timesteps=256)
```

Configure TRPL py passing `projection_kwargs` to TRPL:

```python
model = TRPL("MlpPolicy", env_id, n_steps=128, seed=0, policy_kwargs=dict(net_arch=[16]), projection_class=projection, projection_kwargs={'mean_bound': mean_bound, 'cov_bound': cov_bound}, verbose=1)
```

For available projection_kwargs have a look at [Metastable Projections](https://git.dominik-roth.eu/dodox/metastable-projections).

### Full Covariance

SB3 does not support full covariances (only diagonal). We still provide support for full covariances via the seperate [PCA](https://git.dominik-roth.eu/dodox/PriorConditionedAnnealing) package. (But since we don't actually want to use PCA ('Prior Conditioned Annealing'), we pass 'skip_conditioning=True'; this will lead to the underlying Noise being used directly.)

We therefore pass `use_pca=True` and `policy_kwargs.dist_kwargs = {'Base_Noise': 'WHITE', par_strength: 'FULL', skip_conditioning=True}`

```python
# We support PPO and TRPL, (SAC is untested, we are open to PRs fixing issues)
model = TRPL("MlpPolicy", env_id, n_steps=128, seed=0, use_pca=True, policy_kwargs=dict(net_arch=[16], dist_kwargs={'par_strength': 'FULL', 'skip_conditioning': True}), projection_class=projection, verbose=1)

model.learn(total_timesteps=256)
```

The supported values for `par_strength` are:
- `SCALAR`: We only learn a single scalar value, that is used along the whole diagonal. No covariance is modeled.

- `DIAG`: We learn a diagonal covariance matrix. (e.g. only variances).

- `FULL`: We learn a full covariance matrix, induced via Cholesky decomp (except when Wasserstein Projection is used; then we use the Cholesky of the SPD matrix sqrt of the covariance marix).

- `CONT_SCALAR`: Same as `SCALAR`, but the scalar is not global, it is parameterized by the policy net (contextual).

- `CONT_DIAG`: Same as `DIAG`, but the values are not global, they are parameterized by the policy net.

- `CONT_HYBRID`: We learn a parameric diagonal, that is scaled by the policy net.

- `CONT_FULL`: Same as `FULL`, but parameterized by the policy net.


## License

Since this Repo is an extension to [Stable Baselines 3 by DLR-RM](https://github.com/DLR-RM/stable-baselines3), it contains some of it's code. SB3 is licensed under the [MIT-License](https://github.com/DLR-RM/stable-baselines3/blob/master/LICENSE), and so are our extensions.