From f653c61c03a417cecda7979f3458a543c6afa640 Mon Sep 17 00:00:00 2001
From: Maple-YZ <zihanyee@gmail.com>
Date: Sun, 29 Mar 2026 02:29:01 +0800
Subject: [PATCH] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E7=BB=98=E5=88=B6=E6=A8=A1?=
 =?UTF-8?q?=E5=9E=8B=E6=8E=A7=E5=88=B6action=E6=97=B6=E7=A9=BA=E5=9B=BE?=
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---
 evaluate_models.py |  9 +++++++++
 plot_actions.py    | 32 ++++++++++++++++++++++++++++++++
 2 files changed, 41 insertions(+)
 create mode 100644 plot_actions.py

diff --git a/evaluate_models.py b/evaluate_models.py
index baddc84..c3dde69 100644
--- a/evaluate_models.py
+++ b/evaluate_models.py
@@ -64,6 +64,7 @@ def evaluate_model(model_type, checkpoint_dir, config):
     state = env.reset(seed=42)
     speed_data = []
     density_data = []
+    action_data = []
     brake_counts = []
     speed_variances = []
     throughputs = []
@@ -77,6 +78,7 @@ def evaluate_model(model_type, checkpoint_dir, config):
         else:
             action, _, _ = agent.select_action(state, deterministic=True)
 
+        action_data.append(action.copy())
         state, reward, done, info = env.step(action)
 
         if not done:
@@ -132,6 +134,7 @@ def evaluate_model(model_type, checkpoint_dir, config):
     return {
         "speed_data": np.array(speed_data),
         "density_data": np.array(density_data),
+        "action_data": np.array(action_data),
         "brake_counts": brake_counts,
         "speed_variances": speed_variances,
         "throughputs": throughputs
@@ -150,6 +153,9 @@ def plot_results(results_dict, output_dir):
         density_df = pd.DataFrame(results["density_data"])
         density_df.to_csv(os.path.join(output_dir, f"{name}_density.csv"), index=False)
 
+        action_df = pd.DataFrame(results["action_data"])
+        action_df.to_csv(os.path.join(output_dir, f"{name}_action.csv"), index=False)
+
         metrics_df = pd.DataFrame({
             "brake_counts": results["brake_counts"],
             "speed_variances": results["speed_variances"],
@@ -252,6 +258,7 @@ def evaluate_no_control(config):
     state = env.reset(seed=42)
     speed_data = []
     density_data = []
+    action_data = []
     brake_counts = []
     speed_variances = []
     throughputs = []
@@ -259,6 +266,7 @@ def evaluate_no_control(config):
 
     while not done:
         action = np.array([4] * env.num_edges)
+        action_data.append(action.copy())
         state, reward, done, info = env.step(action)
 
         if not done:
@@ -306,6 +314,7 @@ def evaluate_no_control(config):
     return {
         "speed_data": np.array(speed_data),
         "density_data": np.array(density_data),
+        "action_data": np.array(action_data),
         "brake_counts": brake_counts,
         "speed_variances": speed_variances,
         "throughputs": throughputs
diff --git a/plot_actions.py b/plot_actions.py
new file mode 100644
index 0000000..f5b54b5
--- /dev/null
+++ b/plot_actions.py
@@ -0,0 +1,32 @@
+"""从CSV绘制决策时空图"""
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use("Agg")
+
+output_dir = "evaluation_results"
+models = ["PPO", "APPO", "DQN", "No Control"]
+speed_map = [40, 60, 80, 100, 120]
+
+fig, axes = plt.subplots(2, 2, figsize=(16, 12))
+axes = axes.flatten()
+
+for idx, model in enumerate(models):
+    csv_path = os.path.join(output_dir, f"{model}_action.csv")
+    df = pd.read_csv(csv_path)
+    actions = df.values
+    vsl_speeds = np.vectorize(lambda x: speed_map[int(x)])(actions)
+
+    im = axes[idx].imshow(vsl_speeds.T, aspect="auto", cmap="RdYlGn",
+                          vmin=40, vmax=120, origin="lower")
+    axes[idx].set_title(f"{model} VSL Decision")
+    axes[idx].set_xlabel("Time Step")
+    axes[idx].set_ylabel("Control Edge Index")
+    cbar = plt.colorbar(im, ax=axes[idx], label="VSL (km/h)")
+    cbar.set_ticks([40, 60, 80, 100, 120])
+
+plt.tight_layout()
+plt.savefig(os.path.join(output_dir, "action_heatmaps.png"), dpi=150)
+print("决策时空图已保存")