feat: automatic docs deployment

.github/workflows/ci.yml

@@ -0,0 +1,29 @@
+name: ci
+on:
+  push:
+    branches:
+      - master
+      - main
+permissions:
+  contents: write
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Configure Git Credentials
+        run: |
+          git config user.name github-actions[bot]
+          git config user.email 41898282+github-actions[bot]@users.noreply.github.com
+      - uses: actions/setup-python@v5
+        with:
+          python-version: 3.x
+      - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
+      - uses: actions/cache@v4
+        with:
+          key: mkdocs-material-${{ env.cache_id }}
+          path: .cache
+          restore-keys: |
+            mkdocs-material-
+      - run: pip install mkdocs-material
+      - run: mkdocs gh-deploy --force

README.md

@@ -162,9 +162,7 @@ We summarize the most important elements of the code structure, for users wantin
 
 <pre><code>
 ├── <b>src:</b> Algorithm code (training, network, replay buffer, etc.)
-│   ├── <b>train.py:</b> Main file. Collects trajectories, trains networks, runs evaluations.
-│   ├── <b>losses.py:</b> Contains energy functions, and actor, critic, and alpha losses.
-│   ├── <b>networks.py:</b> Contains network definitions for policy, and encoders for the critic.
+│   ├── <b>train.py:</b> Main file. Defines energy functions + losses, and networks. Collects trajectories, trains networks, runs evaluations.
 │   ├── <b>replay_buffer.py:</b> Contains replay buffer, including logic for state, action, and goal sampling for training.
 │   └── <b>evaluator.py:</b> Runs evaluation and collects metrics.
 ├── <b>envs:</b> Environments (python files and XMLs)