diff --git a/celeste/plot-actual.py b/celeste/plot-actual.py
new file mode 100644
index 0000000..f034b3c
--- /dev/null
+++ b/celeste/plot-actual.py
@@ -0,0 +1,79 @@
+import torch
+import numpy as np
+from pathlib import Path
+import matplotlib.pyplot as plt
+from multiprocessing import Pool
+
+from celeste import Celeste
+from main import DQN
+from main import Transition
+
+# Use cpu, this script is faster in parallel.
+compute_device = torch.device("cpu")
+
+input_model = Path("model_data/after_change")
+
+out_dir = input_model / "plots/actual_reward"
+out_dir.mkdir(parents = True, exist_ok = True)
+
+
+checkpoint = torch.load(
+	input_model / "model.torch",
+	map_location = compute_device
+)
+memory = checkpoint["memory"]
+
+r = np.zeros((128, 128, 8), dtype=np.float32)
+
+for m in memory:
+	x, y, x_target, y_target = list(m.state[0])
+
+	action = m.action[0].item()
+	str_action = Celeste.action_space[action]
+	x = int(x.item())
+	y = int(y.item())
+	x_target = int(x_target.item())
+	y_target = int(y_target.item())
+
+	if (x_target, y_target) != (60, 80):
+		continue
+
+	if m.reward[0].item() == 1:
+		r[y][x][action] += 1
+	else:
+		r[y][x][action] -= 1
+
+
+fig, axs = plt.subplots(2, 4, figsize = (20, 10))
+
+
+# Plot predictions
+for a in range(len(axs.ravel())):
+	ax = axs.ravel()[a]
+	ax.set(
+		adjustable = "box",
+		aspect = "equal",
+		title = Celeste.action_space[a]
+	)
+
+	plot = ax.pcolor(
+		r[:,:,a],
+		cmap = "seismic_r",
+		vmin = -10,
+		vmax = 10
+	)
+
+	ax.plot(60, 80, "k.")
+	#ax.annotate(
+	#	"Target",
+	#	(60, 80),
+	#	textcoords = "offset points",
+	#	xytext = (0, -20),
+	#	ha = "center"
+	#)
+
+	ax.invert_yaxis()
+	fig.colorbar(plot)
+
+fig.savefig(out_dir / "actual.png")
+plt.close()
diff --git a/celeste/plots.py b/celeste/plots.py
index ac81346..4e76e82 100644
--- a/celeste/plots.py
+++ b/celeste/plots.py
@@ -11,10 +11,12 @@ from main import Transition
 # Use cpu, this script is faster in parallel.
 compute_device = torch.device("cpu")
 
-out_dir = Path("out/plots")
+input_model = Path("model_data/current")
+
+src_dir = input_model / "model_archive"
+out_dir = input_model_dir / "plots/predicted_value"
 out_dir.mkdir(parents = True, exist_ok = True)
 
-src_dir = Path("model_data/current/model_archive")
 
 
 def plot(src):
@@ -23,7 +25,10 @@ def plot(src):
 		len(Celeste.action_space)
 	).to(compute_device)
 
-	checkpoint = torch.load(src)
+	checkpoint = torch.load(
+		src,
+		map_location = compute_device
+	)
 	policy_net.load_state_dict(checkpoint["policy_state_dict"])