Merge pull request #79 from RyanNavillus/puf07-nmmomg

Add Syllabus Integration

reinforcement_learning/environment.py

@@ -1,13 +1,17 @@
 from argparse import Namespace
 
+import nmmo
+import nmmo.core.config as nc
+import nmmo.core.game_api as ng
 import pufferlib
 import pufferlib.emulation
-
 from pettingzoo.utils.wrappers.base_parallel import BaseParallelWrapper
+from syllabus.core import PettingZooMultiProcessingSyncWrapper
+from syllabus_task_wrapper import NMMOTaskWrapper
 
-import nmmo
-import nmmo.core.config as nc
-import nmmo.core.game_api as ng
+
+def alt_combat_damage_formula(offense, defense, multiplier, minimum_proportion):
+    return int(max(multiplier * offense - defense, offense * minimum_proportion))
 
 
 class Config(
@@ -23,7 +27,6 @@ class Config(
     nc.Exchange,
 ):
     """Configuration for Neural MMO."""
-
     def __init__(self, env_args: Namespace):
         super().__init__()
 
@@ -48,11 +51,23 @@ def __init__(self, env_args: Namespace):
         self.set("CURRICULUM_FILE_PATH", env_args.curriculum_file_path)
 
 
-def make_env_creator(reward_wrapper_cls: BaseParallelWrapper):
+def make_env_creator(reward_wrapper_cls: BaseParallelWrapper, task_wrapper=False, curriculum=None):
     def env_creator(*args, **kwargs):
         """Create an environment."""
         env = nmmo.Env(Config(kwargs["env"]))  # args.env is provided as kwargs
         env = reward_wrapper_cls(env, **kwargs["reward_wrapper"])
+
+        # Add Syllabus task wrapper
+        if task_wrapper or curriculum is not None:
+            env = NMMOTaskWrapper(env)
+
+        # Use curriculum if provided
+        if curriculum is not None:
+            # Add Syllabus Sync Wrapper
+            env = PettingZooMultiProcessingSyncWrapper(
+                env, curriculum.get_components(), update_on_step=False, task_space=env.task_space,
+            )
+
         env = pufferlib.emulation.PettingZooPufferEnv(env)
         return env
 

reinforcement_learning/stat_wrapper.py

@@ -21,6 +21,9 @@ def __init__(
         self._reset_episode_stats()
         self._stat_prefix = stat_prefix
 
+    def seed(self, seed):
+        self.env.seed(seed)
+
     def observation(self, agent_id, agent_obs):
         """Called before observations are returned from the environment
         Use this to define custom featurizers. Changing the space itself requires you to