firs commit for our new MARL algorithms library, contains working implementations of IAC, SNAC and SEAC

studies/normalization_study.py

@@ -0,0 +1,24 @@
+from algorithms.utils import Checkpointer
+from pathlib import Path
+from algorithms.utils import load_yaml_file, add_env_props, instantiate_class, load_class
+from algorithms.marl import LoopSNAC, LoopIAC, LoopSEAC
+
+
+#study_root = Path(__file__).parent / 'curious_study'
+study_root = Path('/Users/romue/PycharmProjects/EDYS/algorithms/marl')
+
+for i in range(0, 5):
+    for name in ['example_config']:
+        cfg = load_yaml_file(study_root / f'{name}.yaml')
+        add_env_props(cfg)
+
+        env = instantiate_class(cfg['env'])
+        net = instantiate_class(cfg['agent'])
+        max_steps = cfg['algorithm']['max_steps']
+        n_steps = cfg['algorithm']['n_steps']
+
+        checkpointer = Checkpointer(f'{name}#{i}', study_root, cfg, max_steps, 250)
+
+        loop = load_class(cfg['method'])(cfg)
+        df = loop.train_loop(checkpointer)
+