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

studies/viz_policy.py

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+import pandas as pd
+from algorithms.marl import LoopSNAC, LoopIAC, LoopSEAC
+from pathlib import Path
+from algorithms.utils import load_yaml_file
+from tqdm import trange
+study = 'curious_study'
+study_root = Path(__file__).parent / study
+
+#['L2NoAh_gru', 'L2NoCh_gru', 'nomix_gru']:
+render = True
+eval_eps = 3
+for run in range(0, 5):
+    for name in ['basic_gru']:#['L2OnlyAh_gru', 'L2OnlyChAh_gru', 'L2OnlyMix_gru']: #['layernorm_gru', 'basic_gru', 'nonorm_gru', 'spectralnorm_gru']:
+        cfg = load_yaml_file(Path(__file__).parent / study / f'{name}.yaml')
+        p_root = Path(study_root / f'{name}#{run}')
+        dfs = []
+        for i in trange(500):
+            path = p_root / f'checkpoint_{i}'
+
+            snac = LoopSEAC(cfg)
+            snac.load_state_dict(path)
+            snac.eval()
+
+            df = snac.eval_loop(render=render, n_episodes=eval_eps)
+            df['checkpoint'] = i
+            dfs.append(df)
+
+        results = pd.concat(dfs)
+        results['run'] = run
+        results.to_csv(p_root / 'results.csv', index=False)
+
+#sns.lineplot(data=results, x='checkpoint', y='reward', hue='agent', palette='husl')
+
+#plt.savefig(f'{experiment_name}.png')