comments clean up

NeuralMMO · Nov 2, 2023 · f06fab5 · f06fab5
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diff --git a/evaluate.py b/evaluate.py
@@ -112,7 +112,6 @@ def make_policy(envs):
         # Assign the task to the env
         tasks = make_task_from_spec(nmmo_env.possible_agents,
                                     [select_task] * len(nmmo_env.possible_agents))
-        #nmmo_env.reset(make_task_fn=lambda: tasks)
         nmmo_env.tasks = tasks  # this is a hack
         print("seed:", args.seed,
               ", task:", nmmo_env.tasks[0].spec_name)

diff --git a/train.py b/train.py
@@ -79,17 +79,13 @@ def curriculum_generation_track(trainer, args, use_elm=True):
     if use_elm:
         from curriculum_generation import manual_curriculum
         from curriculum_generation.elm import OpenELMTaskGenerator
-        AGENT_MODEL_PATH = ""
         NUM_SEED_TASKS = 20
         NUM_NEW_TASKS = 5
         ELM_DEBUG = True
 
         task_encoder = TaskEncoder(LLM_CHECKPOINT, manual_curriculum, batch_size=2)
         task_generator = OpenELMTaskGenerator(manual_curriculum.curriculum, LLM_CHECKPOINT)
 
-        # @daveey: We need a baseline checkpoint for this
-        #load_agent_model(AGENT_MODEL_PATH)
-
         # Generating new tasks and evaluating all candidate training tasks
         for _ in range(3):
             # NOTE: adjust NUM_SEED_TASKS to fit your gpu