Update gymnasium registration

docs/buildingenv.md

@@ -55,4 +55,27 @@ More instructions coming soon!
 
 ### Custom RL Loop
 
-Coming soon!
+```python
+from sustaingym.envs.building import BuildingEnv, ParameterGenerator
+
+params = ParameterGenerator(
+    building='OfficeSmall', weather='Hot_Dry', location='Tucson')
+env = BuildingEnv(params)
+
+num_hours = 24
+obs, _ = env.reset(seed=123)
+for _ in range(num_hours):
+    action = env.action_space.sample()
+    obs, reward, terminated, truncated, info = env.step(action)
+```
+
+If you prefer using [`gym.make()`](https://gymnasium.farama.org/api/registry/), you may instead create the environment as follows:
+
+```python
+import gymnasium as gym
+from sustaingym.envs.building import ParameterGenerator
+
+params = ParameterGenerator(
+    building='OfficeSmall', weather='Hot_Dry', location='Tucson')
+env = gym.make('sustaingym/Building-v0', parameters=params)
+```

docs/cogenenv.md

@@ -57,6 +57,16 @@ while not terminated:
     obs, reward, terminated, truncated, info = env.step(action)
 ```
 
+If you prefer using [`gym.make()`](https://gymnasium.farama.org/api/registry/), you may instead create the environment as follows:
+
+```python
+import gymnasium as gym
+import sustaingym
+
+rm = 300  # 300 MW renewables penetration
+env = gym.make('sustaingym/Cogen-v0', renewables_magnitude=rm)
+```
+
 
 ### Using our training script
 

docs/evchargingenv.md

@@ -47,6 +47,16 @@ while not terminated:
     obs, reward, terminated, truncated, info = env.step(action)
 ```
 
+If you prefer using [`gym.make()`](https://gymnasium.farama.org/api/registry/), you may instead create the environment as follows:
+
+```python
+import gymnasium as gym
+from sustaingym.envs.evcharging import GMMsTraceGenerator
+
+gmmg = GMMsTraceGenerator('caltech', 'Summer 2019')
+env = gym.make('sustaingym/EVCharging-v0', data_generator=gmmg)
+```
+
 ### Using our training script
 
 1. Install [miniconda3](https://docs.conda.io/en/latest/miniconda-other-installer-links.html).

sustaingym/__init__.py

@@ -2,15 +2,28 @@
 
 register(
     id='sustaingym/EVCharging-v0',
-    entry_point='sustaingym.envs:EVChargingEnv'
+    entry_point='sustaingym.envs.evcharging:EVChargingEnv',
+    nondeterministic=False
 )
 
+# register(
+#     id='sustaingym/ElectricityMarket-v0',
+#     entry_point='sustaingym.envs.electricitymarket:ElectricityMarketEnv'
+# )
+
+# register(
+#     id='sustaingym/DataCenter-v0',
+#     entry_point='sustaingym.envs:DataCenterEnv'
+# )
+
 register(
-    id='sustaingym/ElectricityMarket-v0',
-    entry_point='sustaingym.envs:ElectricityMarketEnv'
+    id='sustaingym/Building-v0',
+    entry_point='sustaingym.envs.building:BuildingEnv',
+    nondeterministic=False
 )
 
 register(
-    id='sustaingym/DataCenter-v0',
-    entry_point='sustaingym.envs:DataCenterEnv'
+    id='sustaingym/Cogen-v0',
+    entry_point='sustaingym.envs.cogen:CogenEnv',
+    nondeterministic=False
 )

sustaingym/envs/building/env.py

@@ -6,7 +6,6 @@
 from typing import Any
 
 import gymnasium as gym
-from gymnasium.envs.registration import EnvSpec
 import numpy as np
 from numpy import linalg as LA
 from scipy.linalg import expm
@@ -194,13 +193,6 @@ def __init__(self, parameters: dict[str, Any]):
         self.A_d = expm(A * self.timestep)
         self.BD_d = LA.inv(A) @ (self.A_d - np.eye(self.A_d.shape[0])) @ BD
 
-        # Define environment spec
-        self.spec = EnvSpec(
-            id='sustaingym/BuildingEnv-v0',
-            entry_point='sustaingym.envs:BuildingEnv',
-            nondeterministic=False,
-            max_episode_steps=288)
-
     def step(self, action: np.ndarray
              ) -> tuple[np.ndarray, float, bool, bool, dict[str, Any]]:
         """Steps the environment.

sustaingym/envs/cogen/env.py

@@ -91,12 +91,6 @@ def __init__(self,
         self.timesteps_per_day = len(self.ambients_dfs[0])
         assert (0 <= self.forecast_horizon < self.timesteps_per_day - 1), 'forecast_horizon must be in [0, timesteps_per_day - 1)'
 
-        self.spec = gym.envs.registration.EnvSpec(
-            id='sustaingym/CogenEnv-v0',
-            entry_point='sustaingym.envs:CogenEnv',
-            nondeterministic=False,
-            max_episode_steps=self.timesteps_per_day)
-
         # actual ONNX model is loaded in reset()
         self._model: rt.InferenceSession | None = None
 

sustaingym/envs/evcharging/env.py

@@ -10,7 +10,6 @@
 import acnportal.acnsim as acns
 import cvxpy as cp
 from gymnasium import Env, spaces
-from gymnasium.envs.registration import EnvSpec
 import numpy as np
 
 from .event_generation import AbstractTraceGenerator
@@ -75,7 +74,7 @@ class EVChargingEnv(Env):
         action_space: spaces.Box, structure of actions expected by env
         observation_space: spaces.Dict, structure of observations
         reward_range: tuple[float, float], min and max rewards
-        spec: EnvSpec, info used to initialize env from gymnasium.make()
+        spec: EnvSpec, info about env if initialized from gymnasium.make()
         metadata: dict[str, Any], unused
         np_random: np.random.Generator, random number generator for the env
 
@@ -175,13 +174,6 @@ def __init__(self, data_generator: AbstractTraceGenerator,
         # Define reward range
         self.reward_range = (-np.inf, self.PROFIT_FACTOR * 32 * self.num_stations)
 
-        # Define environment spec
-        self.spec = EnvSpec(
-            id='sustaingym/EVCharging-v0',
-            entry_point='sustaingym.envs:EVChargingEnv',
-            nondeterministic=False,
-            max_episode_steps=288)
-
         # Set up action projection
         if self.project_action_in_env:
             self._init_action_projection()