Fix issues where the initialized controllers could be different. Created a controller_base.pth that is used for all controller initialization

analysis/main.py

@@ -14,8 +14,8 @@ initial_conditions = {
     "extreme_perturbation": (4*np.pi, 0.0, 0.0, 0),
 }
 loss_functions = ["constant", "linear", "quadratic", "cubic", "inverse", "inverse_squared", "inverse_cubed"]
-epoch_range = (0, 1000)  # Start and end of epoch range
+epoch_range = (0, 3)  # Start and end of epoch range
-epoch_step = 5          # Interval between epochs
+epoch_step = 1          # Interval between epochs
 dt = 0.02                # Time step for simulation
 num_steps = 500          # Number of steps in each simulation
 
@@ -26,11 +26,11 @@ if __name__ == "__main__":
     for condition_name, initial_condition in initial_conditions.items():
         condition_text = f"IC_{'_'.join(map(lambda x: str(round(x, 2)), initial_condition))}"
         desired_theta = initial_condition[-1]
-        condition_path = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/analysis/average_normalized/{condition_name}"
+        condition_path = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/analysis/max_normalized/{condition_name}"
         os.makedirs(condition_path, exist_ok=True)  # Create directory if it does not exist
         for loss_function in loss_functions:
             # Construct the path to the controller directory
-            directory = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/average_normalized/{loss_function}/controllers"
+            directory = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/max_normalized/{loss_function}/controllers"
             # Fetch the controller files according to the specified range and interval
             controllers = get_controller_files(directory, epoch_range, epoch_step)
             # Pack parameters for parallel processing
@@ -53,6 +53,7 @@ if __name__ == "__main__":
                 all_results[loss_function] = {}
             all_results[loss_function][condition_name] = (epochs, theta_over_epochs)
 
+            # continue
             # Plotting the 3D epoch evolution
             print(f"Plotting the 3d epoch evolution for {loss_function} under {condition_text}")
             title = f"Pendulum Angle Evolution for {loss_function} and {condition_text}"
@@ -62,6 +63,8 @@ if __name__ == "__main__":
             print("")
 
         # Plot the  theta as a function of epoch for all loss functions
+        continue
+
         specific_theta_index = num_steps // 2
         save_path = os.path.join(condition_path, f"theta_at_5sec_across_epochs.png")
         plot_theta_vs_epoch(all_results, condition_name, desired_theta, save_path, f"Theta at 5 Seconds across Epochs for {condition_text}", specific_theta_index)
@@ -70,4 +73,9 @@ if __name__ == "__main__":
         save_path = os.path.join(condition_path, f"final_theta_across_epochs.png")
         plot_theta_vs_epoch(all_results, condition_name, desired_theta, save_path, f"Final Theta across Epochs for {condition_text}", specific_theta_index)
 
-        print(f"Completed plotting for all loss functions under {condition_name} condition.\n")
+        print(f"Completed plotting for all loss functions under {condition_name} condition.\n")
+
+    import json
+
+    with open("all_results.json", 'w') as file:
+        json.dump(all_results, file)

analysis/validator.py

@@ -5,7 +5,7 @@ import matplotlib.pyplot as plt
 import pandas as pd
 
 # Load Controller
-controller_file_name = "controller_with_desired_theta.pth"
+controller_file_name = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth"
 
 class PendulumController(nn.Module):
     def __init__(self):
@@ -93,31 +93,32 @@ in_sample_cases = [
     (2/3 * np.pi,    0.0, 0.0, 0.0),
     (-2/3 * np.pi,   0.0, 0.0, 0.0),
 
-    # Omega perturbations
+    # # Omega perturbations
-    (0.0, 1/3 * np.pi,      0.0, 0.0),
+    # (0.0, 1/3 * np.pi,      0.0, 0.0),
-    (0.0, -1/3 * np.pi,     0.0, 0.0),
+    # (0.0, -1/3 * np.pi,     0.0, 0.0),
-    (0.0, 2 * np.pi,        0.0, 0.0),
+    # (0.0, 2 * np.pi,        0.0, 0.0),
-    (0.0, -2 * np.pi,       0.0, 0.0),
+    # (0.0, -2 * np.pi,       0.0, 0.0),
 
-    # Return to non-zero theta
+    # # Return to non-zero theta
-    (0.0, 0.0, 0.0,     2*np.pi),
+    # (0.0, 0.0, 0.0,     2*np.pi),
-    (0.0, 0.0, 0.0,     -2*np.pi),
+    # (0.0, 0.0, 0.0,     -2*np.pi),
-    (0.0, 0.0, 0.0,     1/2 * np.pi),
+    # (0.0, 0.0, 0.0,     1/2 * np.pi),
-    (0.0, 0.0, 0.0,     -1/2 *np.pi),
+    # (0.0, 0.0, 0.0,     -1/2 *np.pi),
-    (0.0, 0.0, 0.0,     1/3 * np.pi),
+    # (0.0, 0.0, 0.0,     1/3 * np.pi),
-    (0.0, 0.0, 0.0,     -1/3 *np.pi),
+    # (0.0, 0.0, 0.0,     -1/3 *np.pi),
 
-    # Mix cases
+    # # Mix cases
-    (1/4 * np.pi,    1 * np.pi,     0.0,      0.0),
+    # (1/4 * np.pi,    1 * np.pi,     0.0,      0.0),
-    (-1/4 * np.pi,   -1 * np.pi,    0.0,     0.0),
+    # (-1/4 * np.pi,   -1 * np.pi,    0.0,     0.0),
-    (1/2 * np.pi,    -1 * np.pi,    0.0,     1/3 * np.pi),
+    # (1/2 * np.pi,    -1 * np.pi,    0.0,     1/3 * np.pi),
-    (-1/2 * np.pi,   1 * np.pi,     0.0,      -1/3 *np.pi),
+    # (-1/2 * np.pi,   1 * np.pi,     0.0,      -1/3 *np.pi),
-    (1/4 * np.pi,    1 * np.pi,     0.0,      2 * np.pi),
+    # (1/4 * np.pi,    1 * np.pi,     0.0,      2 * np.pi),
-    (-1/4 * np.pi,   -1 * np.pi,    0.0,     2 * np.pi),
+    # (-1/4 * np.pi,   -1 * np.pi,    0.0,     2 * np.pi),
-    (1/2 * np.pi,    -1 * np.pi,    0.0,     4 * np.pi),
+    # (1/2 * np.pi,    -1 * np.pi,    0.0,     4 * np.pi),
-    (-1/2 * np.pi,   1 * np.pi,     0.0,      -4 *np.pi),
+    # (-1/2 * np.pi,   1 * np.pi,     0.0,      -4 *np.pi),
 ]
 
+
 # Validation in-sample cases
 print("Performing in-sample validation")
 
@@ -212,25 +213,25 @@ print("\nPerforming out-of-sample validation")
 # Out of sample cases previously generated by numpy
 out_sample_cases = [
     (-2.198958, -4.428501, 0.450833, 0.000000),
-    (1.714196, -0.769896, 0.202738, 0.000000),
+    # (1.714196, -0.769896, 0.202738, 0.000000),
-    (0.241195, -5.493715, 0.438996, 0.000000),
+    # (0.241195, -5.493715, 0.438996, 0.000000),
-    (0.030605, 4.901513, -0.479243, 0.000000),
+    # (0.030605, 4.901513, -0.479243, 0.000000),
-    (1.930445, -1.301926, -0.454050, 0.000000),
+    # (1.930445, -1.301926, -0.454050, 0.000000),
-    (-0.676063, 4.246865, 0.036303, 0.000000),
+    # (-0.676063, 4.246865, 0.036303, 0.000000),
-    (0.734920, -5.925202, 0.047097, 0.000000),
+    # (0.734920, -5.925202, 0.047097, 0.000000),
-    (-3.074471, -3.535424, 0.315438, 0.000000),
+    # (-3.074471, -3.535424, 0.315438, 0.000000),
-    (-0.094486, 6.111091, 0.150525, 0.000000),
+    # (-0.094486, 6.111091, 0.150525, 0.000000),
-    (-1.647671, 5.720526, 0.334181, 0.000000),
+    # (-1.647671, 5.720526, 0.334181, 0.000000),
-    (-2.611260, 5.087704, 0.045460, -3.610785),
+    # (-2.611260, 5.087704, 0.045460, -3.610785),
-    (1.654137, 0.982081, -0.192725, 1.003872),
+    # (1.654137, 0.982081, -0.192725, 1.003872),
-    (-2.394899, 3.550547, -0.430938, 3.261897),
+    # (-2.394899, 3.550547, -0.430938, 3.261897),
-    (0.474917, 0.555166, -0.285173, 1.866752),
+    # (0.474917, 0.555166, -0.285173, 1.866752),
-    (-0.640369, -4.678490, -0.340663, 3.150098),
+    # (-0.640369, -4.678490, -0.340663, 3.150098),
-    (1.747517, -3.248204, -0.001520, 1.221787),
+    # (1.747517, -3.248204, -0.001520, 1.221787),
-    (2.505283, -2.875006, -0.065617, -3.690269),
+    # (2.505283, -2.875006, -0.065617, -3.690269),
-    (1.337244, 2.221707, 0.044979, -2.459730),
+    # (1.337244, 2.221707, 0.044979, -2.459730),
-    (1.531012, 2.230981, -0.291206, -1.924535),
+    # (1.531012, 2.230981, -0.291206, -1.924535),
-    (-1.065792, 4.320740, 0.075405, -1.550644),
+    # (-1.065792, 4.320740, 0.075405, -1.550644),
 ]
 
 

training/average_normalized_trainer.py

@@ -12,14 +12,12 @@ from PendulumDynamics import PendulumDynamics
 
 # Device setup
 device = torch.device("cpu")
+base_controller_path = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth"
 
 # Initial conditions (theta0, omega0, alpha0, desired_theta)
 from initial_conditions import initial_conditions
 state_0 = torch.tensor(initial_conditions, dtype=torch.float32, device=device)
 
-# Device setup
-device = torch.device("cpu")
-
 # Constants
 m = 10.0
 g = 9.81
@@ -30,30 +28,15 @@ t_start, t_end, t_points = 0, 10, 1000
 t_span = torch.linspace(t_start, t_end, t_points, device=device)
 
 # Specify directory for storing results
-output_dir = "average_normalized"
+output_dir = "max_normalized"
 os.makedirs(output_dir, exist_ok=True)
 
-# Use a previously generated random seed
+# Optimizer values
-random_seed = 4529
-
-# Set the seeds for reproducibility
-torch.manual_seed(random_seed)
-np.random.seed(random_seed)
-
-# Print the chosen random seed
-print(f"Random seed for torch and numpy: {random_seed}")
-
-# Initialize controller and dynamics
-controller = PendulumController().to(device)
-pendulum_dynamics = PendulumDynamics(controller, m, R, g).to(device)
-
-# Optimizer setup
 learning_rate = 1e-1
 weight_decay = 1e-4
-optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay)
 
 # Training parameters
-num_epochs = 1001
+num_epochs = 1000
 
 # Define loss functions
 def make_loss_fn(weight_fn):
@@ -89,7 +72,7 @@ weight_functions = {
         'description': 'Quadratic weight: Weights increase cubically from 0 to 1, normalized by the average weight'
     },
     'inverse': {
-        'function': lambda t: ((t+1)**-1 / ((t+1)**-1).max()) / ((t+1)**-2 / ((t+1)**-1).max()).mean(),
+        'function': lambda t: ((t+1)**-1 / ((t+1)**-1).max()) / ((t+1)**-1 / ((t+1)**-1).max()).mean(),
         'description': 'Inverse weight: Weights decrease inversely, normalized by the average weight'
     },
     'inverse_squared': {
@@ -105,7 +88,10 @@ weight_functions = {
 # Training loop for each weight function
 for name, weight_info in weight_functions.items():
     controller = PendulumController().to(device)
+    controller.load_state_dict(torch.load(base_controller_path))
     pendulum_dynamics = PendulumDynamics(controller, m, R, g).to(device)
+    print(f"Loaded {base_controller_path} as base controller")
+
     optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay)
     loss_fn = make_loss_fn(weight_info['function'])
 
@@ -123,7 +109,7 @@ for name, weight_info in weight_functions.items():
 
     # Overwrite configuration and log files
     with open(config_file, "w") as f:
-        f.write(f"Random Seed: {random_seed}\n")
+        f.write(f"Base controller path: {base_controller_path}\n")
         f.write(f"Time Span: {t_start} to {t_end}, Points: {t_points}\n")
         f.write(f"Learning Rate: {learning_rate}\n")
         f.write(f"Weight Decay: {weight_decay}\n")
@@ -138,13 +124,20 @@ for name, weight_info in weight_functions.items():
     with open(log_file, "w", newline="") as csvfile:
         csv_writer = csv.writer(csvfile)
         csv_writer.writerow(["Epoch", "Loss"])
-
+    
     # Training loop
-    for epoch in range(num_epochs):
+    for epoch in range(0, num_epochs+1):
         optimizer.zero_grad()
         state_traj = odeint(pendulum_dynamics, state_0, t_span, method='rk4')
         loss = loss_fn(state_traj, t_span)
         loss.backward()
+
+        # Save the model before training happens
+        model_file = os.path.join(controllers_dir, f"controller_{epoch}.pth")
+        torch.save(controller.state_dict(), model_file)
+        print(f"{model_file} saved with loss: {loss}")
+
+        # Update the weights and biases
         optimizer.step()
 
         # Logging
@@ -152,9 +145,4 @@ for name, weight_info in weight_functions.items():
             csv_writer = csv.writer(csvfile)
             csv_writer.writerow([epoch, loss.item()])
 
-        # Save the model
-        model_file = os.path.join(controllers_dir, f"controller_{epoch}.pth")
-        torch.save(controller.state_dict(), model_file)
-        print(f"{model_file} saved with loss: {loss}")
-
 print("Training complete. Models and logs are saved under respective directories for each loss function.")

training/generate_base_controller.py

@@ -0,0 +1,18 @@
+import torch
+import numpy as np
+
+from PendulumController import PendulumController
+
+device = torch.device("cpu")
+controller = PendulumController().to(device)
+
+# Use a previously generated random seed
+random_seed = 4529
+
+# Set the seeds for reproducibility
+torch.manual_seed(random_seed)
+np.random.seed(random_seed)
+
+controller = PendulumController().to(device)
+model_file = "controller_base.pth"
+torch.save(controller.state_dict(), model_file)

training/max_normalized/constant/training_config.txt

@@ -1,4 +1,3 @@
-Random Seed: 4529
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001

training/max_normalized/cubic/training_config.txt

@@ -1,4 +1,3 @@
-Random Seed: 4529
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001

training/max_normalized/inverse/training_config.txt

@@ -1,4 +1,3 @@
-Random Seed: 4529
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001

training/max_normalized/inverse_cubed/training_config.txt

@@ -1,4 +1,3 @@
-Random Seed: 4529
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001

training/max_normalized/inverse_squared/training_config.txt

@@ -1,4 +1,3 @@
-Random Seed: 4529
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001

training/max_normalized/linear/training_config.txt

@@ -1,4 +1,3 @@
-Random Seed: 4529
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001

training/max_normalized/quadratic/training_config.txt

@@ -1,4 +1,3 @@
-Random Seed: 4529
 Time Span: 0 to 10, Points: 1000
 Learning Rate: 0.1
 Weight Decay: 0.0001

training/max_normalized_trainer.py

@@ -12,14 +12,12 @@ from PendulumDynamics import PendulumDynamics
 
 # Device setup
 device = torch.device("cpu")
+base_controller_path = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth"
 
 # Initial conditions (theta0, omega0, alpha0, desired_theta)
 from initial_conditions import initial_conditions
 state_0 = torch.tensor(initial_conditions, dtype=torch.float32, device=device)
 
-# Device setup
-device = torch.device("cpu")
-
 # Constants
 m = 10.0
 g = 9.81
@@ -33,27 +31,12 @@ t_span = torch.linspace(t_start, t_end, t_points, device=device)
 output_dir = "max_normalized"
 os.makedirs(output_dir, exist_ok=True)
 
-# Use a previously generated random seed
+# Optimizer values
-random_seed = 4529
-
-# Set the seeds for reproducibility
-torch.manual_seed(random_seed)
-np.random.seed(random_seed)
-
-# Print the chosen random seed
-print(f"Random seed for torch and numpy: {random_seed}")
-
-# Initialize controller and dynamics
-controller = PendulumController().to(device)
-pendulum_dynamics = PendulumDynamics(controller, m, R, g).to(device)
-
-# Optimizer setup
 learning_rate = 1e-1
 weight_decay = 1e-4
-optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay)
 
 # Training parameters
-num_epochs = 1001
+num_epochs = 1000
 
 # Define loss functions
 def make_loss_fn(weight_fn):
@@ -93,11 +76,11 @@ weight_functions = {
         'description': 'Inverse weight: Weights decrease inversely, normalized by max'
     },
     'inverse_squared': {
-        'function': lambda t: (t+1)**-2 / ((t+1)**-1).max(),
+        'function': lambda t: (t+1)**-2 / ((t+1)**-2).max(),
         'description': 'Inverse squared weight: Weights decrease inversely squared, normalized by max'
     },
     'inverse_cubed': {
-        'function': lambda t: (t+1)**-3 / ((t+1)**-1).max(),
+        'function': lambda t: (t+1)**-3 / ((t+1)**-3).max(),
         'description': 'Inverse cubed weight: Weights decrease inversely cubed, normalized by max'
     }
 }
@@ -105,7 +88,9 @@ weight_functions = {
 # Training loop for each weight function
 for name, weight_info in weight_functions.items():
     controller = PendulumController().to(device)
+    controller.load_state_dict(torch.load(base_controller_path))
     pendulum_dynamics = PendulumDynamics(controller, m, R, g).to(device)
+    print(f"Loaded {base_controller_path} as base controller")
 
     optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay)
     loss_fn = make_loss_fn(weight_info['function'])
@@ -124,7 +109,7 @@ for name, weight_info in weight_functions.items():
 
     # Overwrite configuration and log files
     with open(config_file, "w") as f:
-        f.write(f"Random Seed: {random_seed}\n")
+        f.write(f"Base controller path: {base_controller_path}\n")
         f.write(f"Time Span: {t_start} to {t_end}, Points: {t_points}\n")
         f.write(f"Learning Rate: {learning_rate}\n")
         f.write(f"Weight Decay: {weight_decay}\n")
@@ -139,13 +124,21 @@ for name, weight_info in weight_functions.items():
     with open(log_file, "w", newline="") as csvfile:
         csv_writer = csv.writer(csvfile)
         csv_writer.writerow(["Epoch", "Loss"])
-
+    
     # Training loop
-    for epoch in range(num_epochs):
+    for epoch in range(0, num_epochs+1):
         optimizer.zero_grad()
         state_traj = odeint(pendulum_dynamics, state_0, t_span, method='rk4')
         loss = loss_fn(state_traj, t_span)
         loss.backward()
+
+        # Save the model before training on this epoch
+        # Therefore, controller_epoch represents the controller after {epoch} training iterations
+        model_file = os.path.join(controllers_dir, f"controller_{epoch}.pth")
+        torch.save(controller.state_dict(), model_file)
+        print(f"{model_file} saved with loss: {loss}")
+
+        # Update the weights and biases
         optimizer.step()
 
         # Logging
@@ -153,9 +146,4 @@ for name, weight_info in weight_functions.items():
             csv_writer = csv.writer(csvfile)
             csv_writer.writerow([epoch, loss.item()])
 
-        # Save the model
-        model_file = os.path.join(controllers_dir, f"controller_{epoch}.pth")
-        torch.save(controller.state_dict(), model_file)
-        print(f"{model_file} saved with loss: {loss}")
-
 print("Training complete. Models and logs are saved under respective directories for each loss function.")