Improving compatibility with VecCheckNan wrapper by adding get_attr method

Author: levitation

CITATION.cff

@@ -5,7 +5,7 @@ authors:
   given-names: "Roland"
   orcid: "https://orcid.org/0009-0006-4882-4166"
 title: "From homeostasis to resource sharing: Biologically and economically aligned multi-objective multi-agent gridworld-based AI safety benchmarks"
-version: 0.9.2
+version: 0.9.3
 doi: 10.48550/arXiv.2410.00081
 date-released: 2024-09-30
 url: "https://github.com/biological-alignment-benchmarks/zoo_to_gym_multiagent_adapter"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "zoo_to_gym_multiagent_adapter"
-version = "0.9.2"
+version = "0.9.3"
 description = "Zoo to Gym Multi-Agent Adapter"
 readme = "README.md"
 authors = [

zoo_to_gym_multiagent_adapter/multiagent_zoo_to_gym_adapter.py

@@ -71,6 +71,10 @@ def __init__(
     def num_envs(self):     # called by VecCheckNan env
         return 1
 
+    # Called by VecCheckNan env. Lets simulate vectorised env here.
+    def get_attr(self, attr_name, indices = None):
+        return [getattr(self, attr_name)]
+
     def set_model(self, model):
         self.model = model
 

zoo_to_gym_multiagent_adapter/singleagent_zoo_to_gym_adapter.py

@@ -13,6 +13,13 @@ class SingleAgentZooToGymAdapter(gym.Env):
     A wrapper that transforms a PettingZoo environment with exactly one agent
     into a single-agent Gymnasium environment.
     Both Zoo ParallelEnv and Zoo AECEnv (sequential env) are supported.
+
+    Similar to using:
+    env = ss.pettingzoo_env_to_vec_env_v1(env)
+    env = ss.concat_vec_envs_v1(env, num_vec_envs=1, num_cpus=1, 
+                                base_class="stable_baselines3")
+    ... but simpler internally and also automatically supports AECEnv, which 
+    would need yet one more additional wrapper when using SuperSuit.
     """
 
     def __init__(self, zoo_env, agent_id):
@@ -32,6 +39,10 @@ def __init__(self, zoo_env, agent_id):
     def num_envs(self):     # called by VecCheckNan env
         return 1
 
+    # Called by VecCheckNan env. Lets simulate vectorised env here.
+    def get_attr(self, attr_name, indices = None):
+        return [getattr(self, attr_name)]
+
     def reset(self, seed=None, options=None, *args, **kwargs):
         """
         Reset the environment.