Sb3 supersuit bugfix (#1031)

.github/workflows/build-publish.yml

@@ -18,9 +18,6 @@ jobs:
     strategy:
       matrix:
         include:
-        - os: ubuntu-latest
-          python: 37
-          platform: manylinux_x86_64
         - os: ubuntu-latest
           python: 38
           platform: manylinux_x86_64
@@ -41,9 +38,9 @@ jobs:
       with:
         python-version: '3.x'
     - name: Install dependencies
-      run: python -m pip install --upgrade setuptools wheel
+      run: python -m pip install --upgrade setuptools wheel build
     - name: Build wheels
-      run: python setup.py sdist bdist_wheel
+      run: python -m build --sdist --wheel
     - name: Store wheels
       uses: actions/upload-artifact@v2
       with:

.github/workflows/linux-test.yml

@@ -33,8 +33,8 @@ jobs:
           AutoROM -v
       - name: Source distribution test
         run: |
-          pip install build
-          python -m build
+          python -m pip install --upgrade build
+          python -m build --sdist
           pip install dist/*.tar.gz
       - name: Release Test
         run: |

docs/tutorials/sb3/index.md

@@ -6,7 +6,7 @@ title: "Stable-Baselines3"
 
 These tutorials show you how to use the [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) (SB3) library to train agents in PettingZoo environments.
 
-For environments with visual observations, we use a [CNN](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html#stable_baselines3.ppo.CnnPolicy) policy and perform pre-processing steps such as frame-stacking, color reduction, and resizing using [SuperSuit](/api/wrappers/supersuit_wrappers/).
+For environments with visual observation spaces, we use a [CNN](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html#stable_baselines3.ppo.CnnPolicy) policy and perform pre-processing steps such as frame-stacking and resizing using [SuperSuit](/api/wrappers/supersuit_wrappers/).
 
 * [PPO for Knights-Archers-Zombies](/tutorials/sb3/kaz/) _Train agents using PPO in a vectorized environment with visual observations_
 

docs/tutorials/sb3/kaz.md

@@ -10,6 +10,8 @@ We use SuperSuit to create vectorized environments, leveraging multithreading to
 
 After training and evaluation, this script will launch a demo game using human rendering. Trained models are saved and loaded from disk (see SB3's [model saving documentation](https://stable-baselines3.readthedocs.io/en/master/guide/save_format.html)).
 
+If the observation space is visual (`vector_state=False` in `env_kwargs`), we pre-process using color reduction, resizing, and frame stacking, and use a [CNN](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html#stable_baselines3.ppo.CnnPolicy) policy.
+
 ```{eval-rst}
 .. note::
 

tutorials/SB3/connect_four/requirements.txt

@@ -1,3 +1,4 @@
 pettingzoo[classic]>=1.23.1
-stable-baselines3>=2.0.0
+# TODO: update to v2.1.0 once it is released
+stable-baselines3 @ git+https://github.com/DLR-RM/stable-baselines3
 sb3-contrib>=2.0.0

tutorials/SB3/kaz/requirements.txt

@@ -1,3 +1,4 @@
 pettingzoo[butterfly]>=1.23.1
 stable-baselines3>=2.0.0
-supersuit>=3.8.1
+# TODO: update to SS release once it's released
+supersuit @ git+https://github.com/Farama-Foundation/SuperSuit.git

tutorials/SB3/kaz/sb3_kaz_vector.py

@@ -14,7 +14,7 @@
 
 import supersuit as ss
 from stable_baselines3 import PPO
-from stable_baselines3.ppo import MlpPolicy
+from stable_baselines3.ppo import CnnPolicy, MlpPolicy
 
 from pettingzoo.butterfly import knights_archers_zombies_v10
 
@@ -27,31 +27,26 @@ def train(env_fn, steps: int = 10_000, seed: int | None = 0, **env_kwargs):
     # MarkovVectorEnv does not support environments with varying numbers of active agents unless black_death is set to True
     env = ss.black_death_v3(env)
 
-    # Pre-process using SuperSuit (color reduction, resizing and frame stacking)
-    env = ss.resize_v1(env, x_size=84, y_size=84)
-    env = ss.frame_stack_v1(env, 3)
+    # Pre-process using SuperSuit
+    visual_observation = not env.unwrapped.vector_state
+    if visual_observation:
+        # If the observation space is visual, reduce the color channels, resize from 512px to 84px, and apply frame stacking
+        env = ss.color_reduction_v0(env, mode="B")
+        env = ss.resize_v1(env, x_size=84, y_size=84)
+        env = ss.frame_stack_v1(env, 3)
 
     env.reset(seed=seed)
 
     print(f"Starting training on {str(env.metadata['name'])}.")
 
     env = ss.pettingzoo_env_to_vec_env_v1(env)
-    env = ss.concat_vec_envs_v1(env, 8, num_cpus=2, base_class="stable_baselines3")
+    env = ss.concat_vec_envs_v1(env, 8, num_cpus=1, base_class="stable_baselines3")
 
-    # TODO: test different hyperparameters
+    # Use a CNN policy if the observation space is visual
     model = PPO(
-        MlpPolicy,
+        CnnPolicy if visual_observation else MlpPolicy,
         env,
         verbose=3,
-        gamma=0.95,
-        n_steps=256,
-        ent_coef=0.0905168,
-        learning_rate=0.00062211,
-        vf_coef=0.042202,
-        max_grad_norm=0.9,
-        gae_lambda=0.99,
-        n_epochs=5,
-        clip_range=0.3,
         batch_size=256,
     )
 
@@ -70,9 +65,13 @@ def eval(env_fn, num_games: int = 100, render_mode: str | None = None, **env_kwa
     # Evaluate a trained agent vs a random agent
     env = env_fn.env(render_mode=render_mode, **env_kwargs)
 
-    # Pre-process using SuperSuit (color reduction, resizing and frame stacking)
-    env = ss.resize_v1(env, x_size=84, y_size=84)
-    env = ss.frame_stack_v1(env, 3)
+    # Pre-process using SuperSuit
+    visual_observation = not env.unwrapped.vector_state
+    if visual_observation:
+        # If the observation space is visual, reduce the color channels, resize from 512px to 84px, and apply frame stacking
+        env = ss.color_reduction_v0(env, mode="B")
+        env = ss.resize_v1(env, x_size=84, y_size=84)
+        env = ss.frame_stack_v1(env, 3)
 
     print(
         f"\nStarting evaluation on {str(env.metadata['name'])} (num_games={num_games}, render_mode={render_mode})"
@@ -125,10 +124,8 @@ def eval(env_fn, num_games: int = 100, render_mode: str | None = None, **env_kwa
 if __name__ == "__main__":
     env_fn = knights_archers_zombies_v10
 
-    # Notes on environment configuration:
-    # max_cycles 100, max_zombies 4, seems to work well (13 points over 10 games)
-    # max_cycles 900 (default) allowed the knights to get kills 1/10 games, but worse archer performance (6 points)
-    env_kwargs = dict(max_cycles=100, max_zombies=4)
+    # Set vector_state to false in order to use visual observations (significantly longer training time)
+    env_kwargs = dict(max_cycles=100, max_zombies=4, vector_state=True)
 
     # Train a model (takes ~5 minutes on a laptop CPU)
     train(env_fn, steps=81_920, seed=0, **env_kwargs)

tutorials/SB3/pistonball/requirements.txt

@@ -1,3 +1,4 @@
 pettingzoo[butterfly]>=1.23.1
 stable-baselines3>=2.0.0
-supersuit>=3.8.1
+# TODO: update to SS release once it's released
+supersuit @ git+https://github.com/Farama-Foundation/SuperSuit.git

tutorials/SB3/waterworld/requirements.txt

@@ -1,4 +1,6 @@
 pettingzoo[sisl]>=1.23.1
 stable-baselines3>=2.0.0
-supersuit>=3.8.1
+# TODO: update to SS release once it's done
+supersuit @ git+https://github.com/Farama-Foundation/SuperSuit.git
+# TODO: remove pymunk requirement before 1.24.0 PZ release, as it will be added as a requirement to sisl
 pymunk