Fix CI (#886)

.github/workflows/linux-tutorials-test.yml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ['3.7', '3.8', '3.9', '3.10', '3.11']
+        python-version: ['3.7', '3.8', '3.9', '3.10']  # '3.11' - broken due to numba
         tutorial: ['CleanRL', 'Tianshou', 'EnvironmentCreation']
     steps:
       - uses: actions/checkout@v3

.pre-commit-config.yaml

@@ -1,18 +1,18 @@
 ---
 repos:
   - repo: https://github.com/python/black
-    rev: 22.8.0
+    rev: 23.1.0
     hooks:
       - id: black
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.1
+    rev: v2.2.2
     hooks:
       - id: codespell
         args:
           - --skip=*.css,*.js,*.map,*.scss,*svg
           - --ignore-words-list=magent
   - repo: https://github.com/PyCQA/flake8
-    rev: 5.0.4
+    rev: 6.0.0
     hooks:
       - id: flake8
         args:
@@ -23,23 +23,23 @@ repos:
           - --show-source
           - --statistics
   - repo: https://github.com/PyCQA/isort
-    rev: 5.10.1
+    rev: 5.12.0
     hooks:
       - id: isort
         args: ["--profile", "black"]
   - repo: https://github.com/asottile/pyupgrade
-    rev: v2.38.0
+    rev: v3.3.1
     hooks:
       - id: pyupgrade
         # TODO: remove `--keep-runtime-typing` option
         args: ["--py37-plus", "--keep-runtime-typing"]
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.3.0
+    rev: v4.4.0
     hooks:
       - id: mixed-line-ending
         args: ["--fix=lf"]
   - repo: https://github.com/pycqa/pydocstyle
-    rev: 6.1.1
+    rev: 6.3.0
     hooks:
       - id: pydocstyle
         args:

pettingzoo/__init__.py

@@ -8,7 +8,6 @@
 # DSP is far more benign (and should probably be the default in SDL anyways)
 
 if sys.platform.startswith("linux"):
-
     os.environ["SDL_AUDIODRIVER"] = "dsp"
 
 os.environ["PYGAME_HIDE_SUPPORT_PROMPT"] = "hide"

pettingzoo/atari/space_invaders/space_invaders.py

@@ -87,7 +87,7 @@ def raw_env(
     zigzaging_bombs=False,
     fast_bomb=False,
     invisible_invaders=False,
-    **kwargs
+    **kwargs,
 ):
     mode = 33 + (
         moving_shields * 1

pettingzoo/butterfly/cooperative_pong/manual_policy.py

@@ -3,7 +3,6 @@
 
 class ManualPolicy:
     def __init__(self, env, agent_id: int = 0, show_obs: bool = False):
-
         self.env = env
         self.agent_id = agent_id
         self.agent = self.env.agents[self.agent_id]

pettingzoo/butterfly/knights_archers_zombies/knights_archers_zombies.py

@@ -211,7 +211,6 @@ def env(**kwargs):
 
 
 class raw_env(AECEnv, EzPickle):
-
     metadata = {
         "render_modes": ["human", "rgb_array"],
         "name": "knights_archers_zombies_v10",
@@ -700,7 +699,6 @@ def step(self, action):
 
         # Do these things once per cycle
         if self._agent_selector.is_last():
-
             # Update the weapons
             self.update_weapons()
 

pettingzoo/butterfly/knights_archers_zombies/manual_policy.py

@@ -3,7 +3,6 @@
 
 class ManualPolicy:
     def __init__(self, env, agent_id: int = 0, show_obs: bool = False):
-
         self.env = env
         self.agent_id = agent_id
         self.agent = self.env.agents[self.agent_id]

pettingzoo/butterfly/pistonball/manual_policy.py

@@ -4,7 +4,6 @@
 
 class ManualPolicy:
     def __init__(self, env, agent_id: int = 0, show_obs: bool = False):
-
         self.env = env
         self.agent_id = agent_id
         self.agent = self.env.agents[self.agent_id]

pettingzoo/butterfly/pistonball/pistonball.py

@@ -122,7 +122,6 @@ def env(**kwargs):
 
 
 class raw_env(AECEnv, EzPickle):
-
     metadata = {
         "render_modes": ["human", "rgb_array"],
         "name": "pistonball_v6",

pettingzoo/classic/chess/chess.py

@@ -107,7 +107,6 @@ def env(render_mode=None):
 
 
 class raw_env(AECEnv):
-
     metadata = {
         "render_modes": ["human", "ansi", "rgb_array"],
         "name": "chess_v5",

pettingzoo/classic/go/go.py

@@ -143,7 +143,6 @@ def env(**kwargs):
 
 
 class raw_env(AECEnv):
-
     metadata = {
         "render_modes": ["human", "rgb_array"],
         "name": "go_v5",

pettingzoo/classic/hanabi/hanabi.py

@@ -379,7 +379,6 @@ def _raise_error_if_config_values_out_of_range(
         observation_type,
         random_start_player,
     ):
-
         if not (2 <= colors <= 5):
             raise ValueError(
                 f"Config parameter {colors} is out of bounds. See description in hanabi.py."

pettingzoo/classic/rlcard_envs/gin_rummy.py

@@ -138,7 +138,6 @@ def env(**kwargs):
 
 
 class raw_env(RLCardBase, EzPickle):
-
     metadata = {
         "render_modes": ["human"],
         "name": "gin_rummy_v4",

pettingzoo/classic/rlcard_envs/leduc_holdem.py

@@ -104,7 +104,6 @@ def env(**kwargs):
 
 
 class raw_env(RLCardBase):
-
     metadata = {
         "render_modes": ["human"],
         "name": "leduc_holdem_v4",

pettingzoo/classic/rlcard_envs/texas_holdem.py

@@ -115,7 +115,6 @@ def env(**kwargs):
 
 
 class raw_env(RLCardBase):
-
     metadata = {
         "render_modes": ["human", "rgb_array"],
         "name": "texas_holdem_v4",

pettingzoo/classic/rlcard_envs/texas_holdem_no_limit.py

@@ -128,7 +128,6 @@ def env(**kwargs):
 
 
 class raw_env(RLCardBase):
-
     metadata = {
         "render_modes": ["human", "rgb_array"],
         "name": "texas_holdem_no_limit_v6",

pettingzoo/classic/rps/rps.py

@@ -451,7 +451,6 @@ def step(self, action):
 
         # collect reward if it is the last agent to act
         if self._agent_selector.is_last():
-
             # same action => 0 reward each agent
             if self.state[self.agents[0]] == self.state[self.agents[1]]:
                 rewards = (0, 0)

pettingzoo/sisl/multiwalker/multiwalker.py

@@ -141,7 +141,6 @@ def env(**kwargs):
 
 
 class raw_env(AECEnv, EzPickle):
-
     metadata = {
         "render_modes": ["human", "rgb_array"],
         "name": "multiwalker_v9",

pettingzoo/sisl/multiwalker/multiwalker_base.py

@@ -214,7 +214,6 @@ def ReportFixture(self, fixture, point, normal, fraction):
         self.lidar = [LidarCallback() for _ in range(10)]
 
     def apply_action(self, action):
-
         self.joints[0].motorSpeed = float(SPEED_HIP * np.sign(action[0]))
         self.joints[0].maxMotorTorque = float(
             MOTORS_TORQUE * np.clip(np.abs(action[0]), 0, 1)
@@ -288,7 +287,6 @@ def action_space(self):
 
 
 class MultiWalkerEnv:
-
     metadata = {"render_modes": ["human", "rgb_array"], "render_fps": FPS}
 
     hardcore = False

pettingzoo/sisl/pursuit/manual_policy.py

@@ -3,7 +3,6 @@
 
 class ManualPolicy:
     def __init__(self, env, agent_id: int = 0, show_obs: bool = False):
-
         self.env = env
         self.agent_id = agent_id
         self.agent = self.env.agents[self.agent_id]

pettingzoo/sisl/pursuit/pursuit.py

@@ -101,7 +101,6 @@ def env(**kwargs):
 
 
 class raw_env(AECEnv, EzPickle):
-
     metadata = {
         "render_modes": ["human", "rgb_array"],
         "name": "pursuit_v4",

pettingzoo/sisl/pursuit/pursuit_base.py

@@ -347,7 +347,7 @@ def draw_agent_counts(self):
             x, y = self.pursuer_layer.get_position(i)
             agent_positions[(x, y)] += 1
 
-        for (x, y) in evader_positions:
+        for x, y in evader_positions:
             (pos_x, pos_y) = (
                 self.pixel_scale * x + self.pixel_scale // 2,
                 self.pixel_scale * y + self.pixel_scale // 2,
@@ -366,7 +366,7 @@ def draw_agent_counts(self):
 
             self.screen.blit(text, (pos_x, pos_y))
 
-        for (x, y) in agent_positions:
+        for x, y in agent_positions:
             (pos_x, pos_y) = (
                 self.pixel_scale * x + self.pixel_scale // 2,
                 self.pixel_scale * y + self.pixel_scale // 2,

pettingzoo/sisl/pursuit/utils/controllers.py

@@ -14,7 +14,6 @@ def act(self, state: np.ndarray) -> int:
 
 
 class RandomPolicy(PursuitPolicy):
-
     # constructor
     def __init__(self, n_actions, rng):
         self.rng = rng

pettingzoo/sisl/pursuit/utils/discrete_agent.py

@@ -9,7 +9,6 @@
 
 
 class DiscreteAgent(Agent):
-
     # constructor
     def __init__(
         self,

pettingzoo/sisl/waterworld/waterworld.py

@@ -155,7 +155,6 @@ def env(**kwargs):
 
 
 class raw_env(AECEnv):
-
     metadata = {
         "render_modes": ["human", "rgb_array"],
         "name": "waterworld_v4",

pettingzoo/test/example_envs/generated_agents_env_v0.py

@@ -17,7 +17,6 @@ def get_type(agent):
 
 
 class raw_env(AECEnv):
-
     metadata = {"render_modes": ["human"], "name": "generated_agents_env_v0"}
 
     def __init__(self, max_cycles=100, render_mode=None):

pettingzoo/test/example_envs/generated_agents_parallel_v0.py

@@ -21,7 +21,6 @@ def get_type(agent):
 
 
 class parallel_env(ParallelEnv):
-
     metadata = {"render_modes": ["human"], "name": "generated_agents_parallel_v0"}
 
     def __init__(self, max_cycles=100, render_mode=None):

pettingzoo/test/state_test.py

@@ -96,7 +96,6 @@ def test_state(env, num_cycles):
 
 
 def test_parallel_env(parallel_env):
-
     parallel_env.reset()
 
     assert isinstance(

test/unwrapped_test.py

@@ -31,7 +31,6 @@ def discrete_observation(env, agents):
 
 @pytest.mark.parametrize(("name", "env_module"), list(all_environments.items()))
 def test_unwrapped(name, env_module):
-
     env = env_module.env(render_mode="human")
     base_env = env.unwrapped
 

tutorials/CleanRL/cleanrl.py

@@ -85,7 +85,6 @@ def unbatchify(x, env):
 
 
 if __name__ == "__main__":
-
     """ALGO PARAMS"""
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     ent_coef = 0.1
@@ -126,18 +125,15 @@ def unbatchify(x, env):
     """ TRAINING LOGIC """
     # train for n number of episodes
     for episode in range(total_episodes):
-
         # collect an episode
         with torch.no_grad():
-
             # collect observations and convert to batch of torch tensors
             next_obs = env.reset(seed=None)
             # reset the episodic return
             total_episodic_return = 0
 
             # each episode has num_steps
             for step in range(0, max_cycles):
-
                 # rollover the observation
                 obs = batchify_obs(next_obs, device)
 

tutorials/Ray/rllib_pistonball.py

@@ -69,7 +69,6 @@ def env_creator(args):
 
 
 if __name__ == "__main__":
-
     env_name = "pistonball_v6"
 
     register_env(env_name, lambda config: ParallelPettingZooEnv(env_creator(config)))

tutorials/Tianshou/2_training_agents.py

@@ -69,7 +69,6 @@ def _get_env():
 
 
 if __name__ == "__main__":
-
     # ======== Step 1: Environment setup =========
     train_envs = DummyVectorEnv([_get_env for _ in range(10)])
     test_envs = DummyVectorEnv([_get_env for _ in range(10)])