Introduce poetry

.github/workflows/action.yaml

@@ -13,18 +13,19 @@ jobs:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
         python-version: [3.7, 3.8, 3.9]
+        poetry-version: [1.1.5]
     steps:
       - name: Checkout
         uses: actions/checkout@v2
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v1
         with:
           python-version: ${{ matrix.python-version }}
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install -r requirements.txt
-          pip install -r handyrl/envs/kaggle/requirements.txt
+      - uses: abatilo/actions-poetry@v2.0.0
+        with:
+          poetry-version: ${{ matrix.poetry-version }}
+      - run: |
+          poetry install
       - name: pytest
         run: |
-          python -m pytest tests
+          poetry run python -m pytest tests

README.md

@@ -48,12 +48,7 @@ cd HandyRL
 
 Then, install additional libraries (e.g. numpy, pytorch). Or run it in a virtual environment or container (e.g. Docker).
 ```
-pip3 install -r requirements.txt
-```
-
-To use games of kaggle environments (e.g. Hungry Geese) you can install also additional dependencies.
-```
-pip3 install -r handyrl/envs/kaggle/requirements.txt
+poetry install
 ```
 
 
@@ -87,7 +82,7 @@ NOTE: [Here is the list of games implemented in HandyRL](handyrl/envs). All para
 After creating the configuration, you can start training by running the following command. The trained models are saved in `models` folder every `update_episodes` described in `config.yaml`.
 
 ```
-python main.py --train
+poetry run python main.py --train
 ```
 
 
@@ -97,7 +92,7 @@ After training, you can evaluate the model against any models. The below code ev
 
 
 ```
-python main.py --eval models/1.pth 100 4
+poetry run python main.py --eval models/1.pth 100 4
 ```
 
 NOTE: Default opponent AI is random agent implemented in `evaluation.py`. You can change the agent with any of your agents.