From 7eb7228a8cf73d1567f8299d50324f2a46d6b873 Mon Sep 17 00:00:00 2001
From: Vasilis Valatsos <vasilvalat@gmail.com>
Date: Tue, 13 Feb 2024 11:48:24 +0100
Subject: [PATCH] Added score_metric and loading options

---
 args.py            |  7 ++++++-
 entities/player.py |  8 ++++----
 main.py            |  4 +++-
 utils/metrics.py   | 28 ----------------------------
 4 files changed, 13 insertions(+), 34 deletions(-)

diff --git a/args.py b/args.py
index 64a9ad4..3e26d88 100644
--- a/args.py
+++ b/args.py
@@ -13,7 +13,7 @@ def parse_args():
     parser.add_argument('--no_seed',
                         default=False,
                         action="store_true",
-                        help="Set to True to run without a seed.")
+                        help="Set to run without a seed.")
 
     parser.add_argument('--seed',
                         type=int,
@@ -83,6 +83,11 @@ def parse_args():
                         action="store_true",
                         help="Set flag to disable learning. Useful for viewing trained agents interact in the environment.")
 
+    parser.add_argument('--load',
+                        type=int,
+                        default=None,
+                        help="Run id to load within chkpt_path")
+    
     parser.add_argument('--show_pg',
                         default=False,
                         action="store_true",
diff --git a/entities/player.py b/entities/player.py
index 9902fea..0ee6379 100644
--- a/entities/player.py
+++ b/entities/player.py
@@ -61,7 +61,7 @@ class Player(pygame.sprite.Sprite):
                     gae_lambda,
                     chkpt_dir,
                     entropy_coef,
-                    no_load=False):
+                    load=None):
 
         self.max_num_enemies = len(self.distance_direction_from_enemy)
         self.get_current_state()
@@ -82,12 +82,12 @@ class Player(pygame.sprite.Sprite):
         print(
             f"\nAgent initialized on player {self.player_id} using {self.agent.actor.device}.")
 
-        if not no_load:
+        if load:
             print("Attempting to load models ...")
             try:
                 self.agent.load_models(
-                    actr_chkpt=f"A{self.player_id}",
-                    crtc_chkpt=f"C{self.player_id}"
+                    actr_chkpt=f"run{load}/A{self.player_id}",
+                    crtc_chkpt=f"run{load}/C{self.player_id}"
                 )
                 print("Models loaded ...\n")
 
diff --git a/main.py b/main.py
index 2de0006..31fa95d 100644
--- a/main.py
+++ b/main.py
@@ -80,7 +80,7 @@ def main():
             gae_lambda=parsed_args.gae_lambda,
             entropy_coef=parsed_args.entropy_coeff,
             chkpt_dir=chkpt_path,
-            no_load=True
+            load=parsed_args.load
         )
 
     # Episodes start
@@ -173,6 +173,8 @@ def main():
 
         metrics.plot_learning_curve(score_history, parsed_args.n_agents, figure_path)
 
+        metrics.plot_score(score_history, parsed_args.n_agents, figure_path)
+
         metrics.plot_loss('actor', actor_loss, parsed_args.n_agents, figure_path)
 
         metrics.plot_loss('critic', critic_loss, parsed_args.n_agents, figure_path)
diff --git a/utils/metrics.py b/utils/metrics.py
index bd8e363..a7e339a 100644
--- a/utils/metrics.py
+++ b/utils/metrics.py
@@ -3,34 +3,6 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 
-def generate(parsed_args):
-
-    # Setup parameter monitoring
-    score_history = np.zeros(
-        shape=(parsed_args.n_agents, parsed_args.n_episodes))
-
-    best_score = np.zeros(parsed_args.n_agents)
-
-    actor_loss = np.zeros(shape=(parsed_args.n_agents,
-                                 parsed_args.n_episodes))
-
-    critic_loss = np.zeros(shape=(parsed_args.n_agents,
-                                  parsed_args.n_episodes))
-
-    total_loss = np.zeros(shape=(parsed_args.n_agents,
-                                 parsed_args.n_episodes))
-
-    entropy = np.zeros(shape=(parsed_args.n_agents,
-                              parsed_args.n_episodes))
-
-    advantage = np.zeros(shape=(parsed_args.n_agents,
-                                parsed_args.n_episodes))
-
-    return score_history, best_score, actor_loss,
-    critic_loss, total_loss, entropy,
-    advantage
-
-
 def plot_learning_curve(scores, num_players, figure_path):
 
     plt.figure()