Merge branch 'improved_linting' into minor_updates

.pre-commit-config.yaml

@@ -9,7 +9,7 @@
 
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
     -   id: check-ast
     -   id: check-yaml
@@ -30,6 +30,7 @@ repos:
     hooks:
       - id: isort
         name: isort
+        args: ['--line-length=110']
 
 -   repo: https://github.com/pre-commit/mirrors-autopep8
     rev: v2.0.4

pyproject.toml

@@ -8,12 +8,12 @@ license = "MIT"
 [tool.poetry.dependencies]
 python = "^3.10"
 matplotlib = "^3.5.1"
-Pillow = "^9.0.0"
 munch = "^2.5.0"
 PyYAML = "^6.0"
 imageio = "^2.14.1"
 dict-deep = "^4.1.2"
 scikit-optimize = "^0.9.0"
+scikit-learn = "^1.3.0"
 gymnasium = "^0.28"
 torch = "^1.10.2"
 gpytorch = "^1.6.0"
@@ -31,5 +31,5 @@ pre-commit = "^3.3.2"
 [tool.poetry.dev-dependencies]
 
 [build-system]
-requires = ["poetry-core @ git+https://github.com/python-poetry/poetry-core.git@master"]
+requires = ["poetry-core @ git+https://github.com/python-poetry/poetry-core.git@main"]
 build-backend = "poetry.core.masonry.api"

safe_control_gym/controllers/ddpg/ddpg.py

@@ -14,18 +14,15 @@
 import torch
 
 from safe_control_gym.controllers.base_controller import BaseController
-from safe_control_gym.controllers.ddpg.ddpg_utils import (
-    DDPGAgent, DDPGBuffer, make_action_noise_process)
-from safe_control_gym.envs.env_wrappers.record_episode_statistics import (
-    RecordEpisodeStatistics, VecRecordEpisodeStatistics)
+from safe_control_gym.controllers.ddpg.ddpg_utils import DDPGAgent, DDPGBuffer, make_action_noise_process
+from safe_control_gym.envs.env_wrappers.record_episode_statistics import (RecordEpisodeStatistics,
+                                                                          VecRecordEpisodeStatistics)
 from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs
-from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import (
-    _flatten_obs, _unflatten_obs)
-from safe_control_gym.math_and_models.normalization import (
-    BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer)
+from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_obs, _unflatten_obs
+from safe_control_gym.math_and_models.normalization import (BaseNormalizer, MeanStdNormalizer,
+                                                            RewardStdNormalizer)
 from safe_control_gym.utils.logging import ExperimentLogger
-from safe_control_gym.utils.utils import (get_random_state, is_wrapped,
-                                          set_random_state)
+from safe_control_gym.utils.utils import get_random_state, is_wrapped, set_random_state
 
 
 class DDPG(BaseController):

safe_control_gym/controllers/lqr/ilqr.py

@@ -8,8 +8,8 @@
 from termcolor import colored
 
 from safe_control_gym.controllers.base_controller import BaseController
-from safe_control_gym.controllers.lqr.lqr_utils import (
-    compute_lqr_gain, discretize_linear_system, get_cost_weight_matrix)
+from safe_control_gym.controllers.lqr.lqr_utils import (compute_lqr_gain, discretize_linear_system,
+                                                        get_cost_weight_matrix)
 from safe_control_gym.envs.benchmark_env import Task
 
 

safe_control_gym/controllers/lqr/lqr.py

@@ -1,8 +1,7 @@
 '''Linear Quadratic Regulator (LQR).'''
 
 from safe_control_gym.controllers.base_controller import BaseController
-from safe_control_gym.controllers.lqr.lqr_utils import (compute_lqr_gain,
-                                                        get_cost_weight_matrix)
+from safe_control_gym.controllers.lqr.lqr_utils import compute_lqr_gain, get_cost_weight_matrix
 from safe_control_gym.envs.benchmark_env import Task
 
 

safe_control_gym/controllers/mpc/gp_mpc.py

@@ -31,9 +31,8 @@
 from skopt.sampler import Lhs
 
 from safe_control_gym.controllers.lqr.lqr_utils import discretize_linear_system
-from safe_control_gym.controllers.mpc.gp_utils import (
-    GaussianProcessCollection, ZeroMeanIndependentGPModel, covSEard,
-    kmeans_centriods)
+from safe_control_gym.controllers.mpc.gp_utils import (GaussianProcessCollection, ZeroMeanIndependentGPModel,
+                                                       covSEard, kmeans_centriods)
 from safe_control_gym.controllers.mpc.linear_mpc import MPC, LinearMPC
 from safe_control_gym.envs.benchmark_env import Task
 
@@ -714,7 +713,7 @@ def learn(self,
                                                  self.train_iterations + validation_iterations,
                                                  random_state=self.seed)
             input_samples = np.array(input_samples)  # not being used currently
-            seeds = self.env.np_random.randint(0, 99999, size=self.train_iterations + validation_iterations)
+            seeds = self.env.np_random.integers(0, 99999, size=self.train_iterations + validation_iterations)
             for i in range(self.train_iterations + validation_iterations):
                 # For random initial state training.
                 # init_state = init_state_samples[i,:]

safe_control_gym/controllers/mpc/linear_mpc.py

@@ -14,8 +14,7 @@
 
 from safe_control_gym.controllers.lqr.lqr_utils import discretize_linear_system
 from safe_control_gym.controllers.mpc.mpc import MPC
-from safe_control_gym.controllers.mpc.mpc_utils import \
-    compute_discrete_lqr_gain_from_cont_linear_system
+from safe_control_gym.controllers.mpc.mpc_utils import compute_discrete_lqr_gain_from_cont_linear_system
 from safe_control_gym.envs.benchmark_env import Task
 
 

safe_control_gym/controllers/mpc/mpc.py

@@ -6,12 +6,11 @@
 import numpy as np
 
 from safe_control_gym.controllers.base_controller import BaseController
-from safe_control_gym.controllers.mpc.mpc_utils import (
-    compute_discrete_lqr_gain_from_cont_linear_system, compute_state_rmse,
-    get_cost_weight_matrix, reset_constraints, rk_discrete)
+from safe_control_gym.controllers.mpc.mpc_utils import (compute_discrete_lqr_gain_from_cont_linear_system,
+                                                        compute_state_rmse, get_cost_weight_matrix,
+                                                        reset_constraints, rk_discrete)
 from safe_control_gym.envs.benchmark_env import Task
-from safe_control_gym.envs.constraints import (GENERAL_CONSTRAINTS,
-                                               create_constraint_list)
+from safe_control_gym.envs.constraints import GENERAL_CONSTRAINTS, create_constraint_list
 
 
 class MPC(BaseController):

safe_control_gym/controllers/ppo/ppo.py

@@ -19,16 +19,14 @@
 import torch
 
 from safe_control_gym.controllers.base_controller import BaseController
-from safe_control_gym.controllers.ppo.ppo_utils import (
-    PPOAgent, PPOBuffer, compute_returns_and_advantages)
-from safe_control_gym.envs.env_wrappers.record_episode_statistics import (
-    RecordEpisodeStatistics, VecRecordEpisodeStatistics)
+from safe_control_gym.controllers.ppo.ppo_utils import PPOAgent, PPOBuffer, compute_returns_and_advantages
+from safe_control_gym.envs.env_wrappers.record_episode_statistics import (RecordEpisodeStatistics,
+                                                                          VecRecordEpisodeStatistics)
 from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs
-from safe_control_gym.math_and_models.normalization import (
-    BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer)
+from safe_control_gym.math_and_models.normalization import (BaseNormalizer, MeanStdNormalizer,
+                                                            RewardStdNormalizer)
 from safe_control_gym.utils.logging import ExperimentLogger
-from safe_control_gym.utils.utils import (get_random_state, is_wrapped,
-                                          set_random_state)
+from safe_control_gym.utils.utils import get_random_state, is_wrapped, set_random_state
 
 
 class PPO(BaseController):

safe_control_gym/controllers/rarl/rap.py

@@ -16,17 +16,15 @@
 import torch
 
 from safe_control_gym.controllers.base_controller import BaseController
-from safe_control_gym.controllers.ppo.ppo_utils import (
-    PPOAgent, PPOBuffer, compute_returns_and_advantages)
+from safe_control_gym.controllers.ppo.ppo_utils import PPOAgent, PPOBuffer, compute_returns_and_advantages
 from safe_control_gym.controllers.rarl.rarl_utils import split_obs_by_adversary
-from safe_control_gym.envs.env_wrappers.record_episode_statistics import (
-    RecordEpisodeStatistics, VecRecordEpisodeStatistics)
+from safe_control_gym.envs.env_wrappers.record_episode_statistics import (RecordEpisodeStatistics,
+                                                                          VecRecordEpisodeStatistics)
 from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs
-from safe_control_gym.math_and_models.normalization import (
-    BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer)
+from safe_control_gym.math_and_models.normalization import (BaseNormalizer, MeanStdNormalizer,
+                                                            RewardStdNormalizer)
 from safe_control_gym.utils.logging import ExperimentLogger
-from safe_control_gym.utils.utils import (get_random_state, is_wrapped,
-                                          set_random_state)
+from safe_control_gym.utils.utils import get_random_state, is_wrapped, set_random_state
 
 
 class RAP(BaseController):

safe_control_gym/controllers/rarl/rarl.py

@@ -16,16 +16,14 @@
 import torch
 
 from safe_control_gym.controllers.base_controller import BaseController
-from safe_control_gym.controllers.ppo.ppo_utils import (
-    PPOAgent, PPOBuffer, compute_returns_and_advantages)
-from safe_control_gym.envs.env_wrappers.record_episode_statistics import (
-    RecordEpisodeStatistics, VecRecordEpisodeStatistics)
+from safe_control_gym.controllers.ppo.ppo_utils import PPOAgent, PPOBuffer, compute_returns_and_advantages
+from safe_control_gym.envs.env_wrappers.record_episode_statistics import (RecordEpisodeStatistics,
+                                                                          VecRecordEpisodeStatistics)
 from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs
-from safe_control_gym.math_and_models.normalization import (
-    BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer)
+from safe_control_gym.math_and_models.normalization import (BaseNormalizer, MeanStdNormalizer,
+                                                            RewardStdNormalizer)
 from safe_control_gym.utils.logging import ExperimentLogger
-from safe_control_gym.utils.utils import (get_random_state, is_wrapped,
-                                          set_random_state)
+from safe_control_gym.utils.utils import get_random_state, is_wrapped, set_random_state
 
 
 class RARL(BaseController):

safe_control_gym/controllers/rarl/rarl_utils.py

@@ -2,8 +2,7 @@
 
 import numpy as np
 
-from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import (
-    _flatten_obs, _unflatten_obs)
+from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_obs, _unflatten_obs
 
 
 def split_obs_by_adversary(obs, indices_splits):

safe_control_gym/controllers/sac/sac.py

@@ -21,16 +21,14 @@
 
 from safe_control_gym.controllers.base_controller import BaseController
 from safe_control_gym.controllers.sac.sac_utils import SACAgent, SACBuffer
-from safe_control_gym.envs.env_wrappers.record_episode_statistics import (
-    RecordEpisodeStatistics, VecRecordEpisodeStatistics)
+from safe_control_gym.envs.env_wrappers.record_episode_statistics import (RecordEpisodeStatistics,
+                                                                          VecRecordEpisodeStatistics)
 from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs
-from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import (
-    _flatten_obs, _unflatten_obs)
-from safe_control_gym.math_and_models.normalization import (
-    BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer)
+from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_obs, _unflatten_obs
+from safe_control_gym.math_and_models.normalization import (BaseNormalizer, MeanStdNormalizer,
+                                                            RewardStdNormalizer)
 from safe_control_gym.utils.logging import ExperimentLogger
-from safe_control_gym.utils.utils import (get_random_state, is_wrapped,
-                                          set_random_state)
+from safe_control_gym.utils.utils import get_random_state, is_wrapped, set_random_state
 
 
 class SAC(BaseController):

safe_control_gym/controllers/safe_explorer/safe_ppo.py

@@ -8,20 +8,16 @@
 import torch
 
 from safe_control_gym.controllers.base_controller import BaseController
-from safe_control_gym.controllers.ppo.ppo_utils import \
-    compute_returns_and_advantages
-from safe_control_gym.controllers.safe_explorer.safe_explorer_utils import (
-    ConstraintBuffer, SafetyLayer)
-from safe_control_gym.controllers.safe_explorer.safe_ppo_utils import (
-    SafePPOAgent, SafePPOBuffer)
-from safe_control_gym.envs.env_wrappers.record_episode_statistics import (
-    RecordEpisodeStatistics, VecRecordEpisodeStatistics)
+from safe_control_gym.controllers.ppo.ppo_utils import compute_returns_and_advantages
+from safe_control_gym.controllers.safe_explorer.safe_explorer_utils import ConstraintBuffer, SafetyLayer
+from safe_control_gym.controllers.safe_explorer.safe_ppo_utils import SafePPOAgent, SafePPOBuffer
+from safe_control_gym.envs.env_wrappers.record_episode_statistics import (RecordEpisodeStatistics,
+                                                                          VecRecordEpisodeStatistics)
 from safe_control_gym.envs.env_wrappers.vectorized_env import make_vec_envs
-from safe_control_gym.math_and_models.normalization import (
-    BaseNormalizer, MeanStdNormalizer, RewardStdNormalizer)
+from safe_control_gym.math_and_models.normalization import (BaseNormalizer, MeanStdNormalizer,
+                                                            RewardStdNormalizer)
 from safe_control_gym.utils.logging import ExperimentLogger
-from safe_control_gym.utils.utils import (get_random_state, is_wrapped,
-                                          set_random_state)
+from safe_control_gym.utils.utils import get_random_state, is_wrapped, set_random_state
 
 
 class SafeExplorerPPO(BaseController):

safe_control_gym/envs/disturbances.py

@@ -100,7 +100,7 @@ def reset(self,
               env
               ):
         if self.step_offset is None:
-            self.current_step_offset = self.np_random.randint(self.max_step)
+            self.current_step_offset = self.np_random.integers(self.max_step)
         else:
             self.current_step_offset = self.step_offset
         self.current_peak_step = int(self.current_step_offset + self.duration / 2)
@@ -146,7 +146,7 @@ def reset(self,
               env
               ):
         if self.step_offset is None:
-            self.current_step_offset = self.np_random.randint(self.max_step)
+            self.current_step_offset = self.np_random.integers(self.max_step)
         else:
             self.current_step_offset = self.step_offset
 

safe_control_gym/envs/env_wrappers/record_episode_statistics.py

@@ -7,8 +7,7 @@
 import gymnasium as gym
 import numpy as np
 
-from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env import \
-    VecEnvWrapper
+from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env import VecEnvWrapper
 
 
 class RecordEpisodeStatistics(gym.Wrapper):

safe_control_gym/envs/env_wrappers/vectorized_env/__init__.py

@@ -5,10 +5,8 @@
 import numpy as np
 import torch
 
-from safe_control_gym.envs.env_wrappers.vectorized_env.dummy_vec_env import \
-    DummyVecEnv
-from safe_control_gym.envs.env_wrappers.vectorized_env.subproc_vec_env import \
-    SubprocVecEnv
+from safe_control_gym.envs.env_wrappers.vectorized_env.dummy_vec_env import DummyVecEnv
+from safe_control_gym.envs.env_wrappers.vectorized_env.subproc_vec_env import SubprocVecEnv
 
 
 def make_env_fn(env_func,

safe_control_gym/envs/env_wrappers/vectorized_env/dummy_vec_env.py

@@ -5,8 +5,7 @@
 import numpy as np
 
 from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env import VecEnv
-from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import \
-    _flatten_obs
+from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import _flatten_obs
 from safe_control_gym.utils.utils import get_random_state, set_random_state
 
 

safe_control_gym/envs/env_wrappers/vectorized_env/subproc_vec_env.py

@@ -11,8 +11,9 @@
 import numpy as np
 
 from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env import VecEnv
-from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import (
-    CloudpickleWrapper, _flatten_list, _flatten_obs, clear_mpi_env_vars)
+from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import (CloudpickleWrapper,
+                                                                             _flatten_list, _flatten_obs,
+                                                                             clear_mpi_env_vars)
 from safe_control_gym.utils.utils import get_random_state, set_random_state
 
 

safe_control_gym/envs/env_wrappers/vectorized_env/vec_env.py

@@ -7,8 +7,7 @@
 
 from abc import ABC, abstractmethod
 
-from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import \
-    tile_images
+from safe_control_gym.envs.env_wrappers.vectorized_env.vec_env_utils import tile_images
 
 
 class VecEnv(ABC):

safe_control_gym/envs/gym_control/cartpole.py

@@ -20,8 +20,7 @@
 from gymnasium import spaces
 
 from safe_control_gym.envs.benchmark_env import BenchmarkEnv, Cost, Task
-from safe_control_gym.envs.constraints import (GENERAL_CONSTRAINTS,
-                                               SymmetricStateConstraint)
+from safe_control_gym.envs.constraints import GENERAL_CONSTRAINTS, SymmetricStateConstraint
 from safe_control_gym.math_and_models.normalization import normalize_angle
 from safe_control_gym.math_and_models.symbolic_systems import SymbolicModel
 

safe_control_gym/envs/gym_pybullet_drones/quadrotor.py

@@ -15,11 +15,9 @@
 from safe_control_gym.envs.benchmark_env import Cost, Task
 from safe_control_gym.envs.constraints import GENERAL_CONSTRAINTS
 from safe_control_gym.envs.gym_pybullet_drones.base_aviary import BaseAviary
-from safe_control_gym.envs.gym_pybullet_drones.quadrotor_utils import (
-    QuadType, cmd2pwm, pwm2rpm)
+from safe_control_gym.envs.gym_pybullet_drones.quadrotor_utils import QuadType, cmd2pwm, pwm2rpm
 from safe_control_gym.math_and_models.symbolic_systems import SymbolicModel
-from safe_control_gym.math_and_models.transformations import (
-    csRotXYZ, transform_trajectory)
+from safe_control_gym.math_and_models.transformations import csRotXYZ, transform_trajectory
 
 
 class Quadrotor(BaseAviary):

safe_control_gym/experiments/base_experiment.py

@@ -9,8 +9,7 @@
 from munch import munchify
 from termcolor import colored
 
-from safe_control_gym.math_and_models.metrics.performance_metrics import \
-    compute_cvar
+from safe_control_gym.math_and_models.metrics.performance_metrics import compute_cvar
 from safe_control_gym.utils.utils import is_wrapped
 
 

safe_control_gym/safety_filters/cbf/cbf.py

@@ -10,9 +10,7 @@
 import numpy as np
 
 from safe_control_gym.safety_filters.base_safety_filter import BaseSafetyFilter
-from safe_control_gym.safety_filters.cbf.cbf_utils import (cartesian_product,
-                                                           cbf_cartpole,
-                                                           linear_function)
+from safe_control_gym.safety_filters.cbf.cbf_utils import cartesian_product, cbf_cartpole, linear_function
 
 
 class CBF(BaseSafetyFilter):

safe_control_gym/safety_filters/mpsc/linear_mpsc.py

@@ -18,13 +18,10 @@
 from safe_control_gym.controllers.lqr.lqr_utils import discretize_linear_system
 from safe_control_gym.controllers.mpc.mpc_utils import rk_discrete
 from safe_control_gym.envs.benchmark_env import Environment, Task
-from safe_control_gym.envs.constraints import (ConstrainedVariableType,
-                                               LinearConstraint,
-                                               QuadraticContstraint)
+from safe_control_gym.envs.constraints import ConstrainedVariableType, LinearConstraint, QuadraticContstraint
 from safe_control_gym.safety_filters.mpsc.mpsc import MPSC
-from safe_control_gym.safety_filters.mpsc.mpsc_utils import (
-    Cost_Function, compute_RPI_set, ellipse_bounding_box,
-    pontryagin_difference_AABB)
+from safe_control_gym.safety_filters.mpsc.mpsc_utils import (Cost_Function, compute_RPI_set,
+                                                             ellipse_bounding_box, pontryagin_difference_AABB)
 
 
 class LINEAR_MPSC(MPSC):