disable learning

This will likely be reverted or squashed away, since I am doing this to
get the synthesis times on the server...

examples/shields/rl/13_minigridsb.py

@@ -65,32 +65,32 @@ def main():
         eval_env = ActionMasker(eval_env, nomask_fn)
     else:
         assert(False) # TODO Do something proper
-    model = MaskablePPO("CnnPolicy", env, verbose=1, tensorboard_log=log_dir, device="auto")
-    model.set_logger(new_logger)
-    steps = args.steps
-
-
-    # Evaluation
-    eval_freq=max(500, int(args.steps/30))
-    n_eval_episodes=5
-    render_freq = eval_freq
-    if shielded_evaluation(args.shielding):
-        from sb3_contrib.common.maskable.evaluation import evaluate_policy
-        evalCallback = MaskableEvalCallback(eval_env, best_model_save_path=log_dir,
-                                            log_path=log_dir, eval_freq=eval_freq,
-                                            deterministic=True, render=False, n_eval_episodes=n_eval_episodes)
-        imageAndVideoCallback = ImageRecorderCallback(eval_env, render_freq, n_eval_episodes=1, evaluation_method=evaluate_policy, log_dir=log_dir, deterministic=True, verbose=0)
-    else:
-        from stable_baselines3.common.evaluation import evaluate_policy
-        evalCallback = EvalCallback(eval_env, best_model_save_path=log_dir,
-                                    log_path=log_dir, eval_freq=eval_freq,
-                                    deterministic=True, render=False, n_eval_episodes=n_eval_episodes)
-
-        imageAndVideoCallback = ImageRecorderCallback(eval_env, render_freq, n_eval_episodes=1, evaluation_method=evaluate_policy, log_dir=log_dir, deterministic=True, verbose=0)
-
-
-    model.learn(steps,callback=[imageAndVideoCallback, InfoCallback(), evalCallback])
-    model.save(f"{log_dir}/{expname(args)}")
+    #model = MaskablePPO("CnnPolicy", env, verbose=1, tensorboard_log=log_dir, device="auto")
+    #model.set_logger(new_logger)
+    #steps = args.steps
+
+
+    ## Evaluation
+    #eval_freq=max(500, int(args.steps/30))
+    #n_eval_episodes=5
+    #render_freq = eval_freq
+    #if shielded_evaluation(args.shielding):
+    #    from sb3_contrib.common.maskable.evaluation import evaluate_policy
+    #    evalCallback = MaskableEvalCallback(eval_env, best_model_save_path=log_dir,
+    #                                        log_path=log_dir, eval_freq=eval_freq,
+    #                                        deterministic=True, render=False, n_eval_episodes=n_eval_episodes)
+    #    imageAndVideoCallback = ImageRecorderCallback(eval_env, render_freq, n_eval_episodes=1, evaluation_method=evaluate_policy, log_dir=log_dir, deterministic=True, verbose=0)
+    #else:
+    #    from stable_baselines3.common.evaluation import evaluate_policy
+    #    evalCallback = EvalCallback(eval_env, best_model_save_path=log_dir,
+    #                                log_path=log_dir, eval_freq=eval_freq,
+    #                                deterministic=True, render=False, n_eval_episodes=n_eval_episodes)
+
+    #    imageAndVideoCallback = ImageRecorderCallback(eval_env, render_freq, n_eval_episodes=1, evaluation_method=evaluate_policy, log_dir=log_dir, deterministic=True, verbose=0)
+
+
+    #model.learn(steps,callback=[imageAndVideoCallback, InfoCallback(), evalCallback])
+    #model.save(f"{log_dir}/{expname(args)}")
 
 
 if __name__ == '__main__':