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Behavior Cloning (BC) Baseline for Transformer-based Unity Agent (Ablation Study)
-This dataset contains the evaluation logs and performance metrics of a Behavior Cloning (BC) baseline model, conducted as an ablation study for our Decision Transformer (DT) research in a Unity ML-Agents environment.

DT Update/MakingBC/train_sequential_ext_RLStep_For_BC.py

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+import torch
+from torch.utils.data import DataLoader, Subset
+from dataset_dt import TrajectoryDataset
+from model_dt import DecisionTransformer
+import glob
+import os
+import gc
+import time
+import argparse
+import sys
+
+def train_sequential_rl_steps_BC(data_prefix="trajectory_data_part_", output_model="dt_model_trained_rl_limited.pth", target_rl_steps=1000000):
+    log_dir = "./logs"
+    seq_len = 32
+    batch_size = 32
+    learning_rate = 1e-4
+    epochs = 3 
+    # Note: Epochs are still iterated, but we will likely break early in the first epoch 
+    # if target_rl_steps is reached. If target is huge, it might run multiple epochs.
+    # But for "1M RL Steps" on a dataset of >2M, it will stop in Epoch 1.
+    
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"[INFO] Using device: {device}")
+
+    # 1. Initialize Model Dimensions
+    pattern = f"{data_prefix}*.pkl"
+    split_files = sorted(glob.glob(pattern))
+    if not split_files:
+        split_files = sorted(glob.glob(os.path.join(log_dir, pattern)))
+        
+    if not split_files:
+        print(f"[ERROR] No files found matching prefix: {data_prefix}")
+        return
+
+    print(f"[INFO] Found {len(split_files)} split files matching '{data_prefix}'.")
+    
+    # Peek at first file for dimensions
+    print("[INFO] Peeking at first file for dimensions...")
+    temp_dataset = TrajectoryDataset(log_dir, seq_len=seq_len, specific_file=split_files[0])
+    if len(temp_dataset) > 0:
+        obs_dim = temp_dataset[0]["observations"].shape[-1]
+        act_dim = temp_dataset[0]["actions"].shape[-1]
+    else:
+        # Fallback defaults
+        obs_dim = 9
+        act_dim = 3
+        
+    del temp_dataset
+    gc.collect()
+    
+    print(f"[INFO] Obs Dim: {obs_dim}, Act Dim: {act_dim}")
+
+    model = DecisionTransformer(
+        obs_dim=obs_dim,
+        act_dim=act_dim,
+        hidden=256,
+        n_layers=4,
+        n_heads=4,
+        max_len=4096 
+    ).to(device)
+
+    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
+    
+    print(f"[INFO] Starting FRESH training with RL STEP LIMIT: {target_rl_steps} (Pre-loading to Memory)")
+
+    # 1. Pre-load Data Phase
+    loaded_datasets = []
+    total_rl_steps_loaded = 0
+    
+    for i, pkl_file in enumerate(split_files):
+        steps_needed = target_rl_steps - total_rl_steps_loaded
+        if steps_needed <= 0:
+            break
+            
+        print(f"[INFO] Pre-loading chunk {i+1}/{len(split_files)}: {pkl_file}")
+        dataset = TrajectoryDataset(log_dir, seq_len=seq_len, specific_file=pkl_file)
+        dataset_len = len(dataset)
+        
+        if dataset_len == 0:
+            continue
+            
+        if dataset_len > steps_needed:
+            print(f"  [LIMIT] Trimming chunk to {steps_needed} samples.")
+            dataset = Subset(dataset, range(steps_needed))
+            loaded_datasets.append(dataset)
+            total_rl_steps_loaded += steps_needed
+            break
+        else:
+            loaded_datasets.append(dataset)
+            total_rl_steps_loaded += dataset_len
+            
+        print(f"  [PROGRESS] Memory Buffer: {total_rl_steps_loaded} / {target_rl_steps}")
+
+    if not loaded_datasets:
+        print("[ERROR] No data loaded! Check file paths.")
+        return
+
+    # 2. Training Phase (Sequential per Chunk, just like original)
+    print(f"[INFO] Data Pre-loading Complete. Starting Training on {len(loaded_datasets)} chunks.")
+    
+    global_gradient_steps = 0
+    
+    for epoch in range(epochs):
+        print(f"\n=== Epoch {epoch+1}/{epochs} ===")
+        epoch_start_time = time.time()
+        
+        for i, dataset in enumerate(loaded_datasets):
+            # Same Batch/Shuffle logic as original
+            loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+            
+            model.train()
+            chunk_loss = 0.0
+            chunk_steps = 0
+            
+            for batch in loader:
+                states = batch['observations'].to(device)
+                actions = batch['actions'].to(device)
+                returns = batch['returns_to_go'].to(device)
+                timesteps = batch['timesteps'].to(device)
+
+                returns = torch.zeros_like(returns)
+                
+                # Forward
+                action_preds = model(
+                    obs=states,
+                    act=actions,
+                    rtg=returns,
+                    timesteps=timesteps
+                )
+                
+                # Loss
+                action_target = batch['target_actions'].to(device)
+                loss = torch.mean((action_preds - action_target) ** 2)
+                
+                optimizer.zero_grad()
+                loss.backward()
+                optimizer.step()
+                
+                chunk_loss += loss.item()
+                chunk_steps += 1
+                global_gradient_steps += 1
+                
+                if chunk_steps % 100 == 0:
+                    print(f"  Grad Step {chunk_steps}, Loss: {loss.item():.4f}", end="\r")
+            
+            avg_chunk_loss = chunk_loss / chunk_steps if chunk_steps > 0 else 0
+            print(f"  Chunk {i+1} Finished. Avg Loss: {avg_chunk_loss:.4f}")
+
+        print(f"Epoch {epoch+1} completed in {time.time() - epoch_start_time:.2f}s.")
+        
+        # Save Model per Epoch
+        dir_name, file_name = os.path.split(output_model)
+        epoch_model_path = os.path.join(dir_name, f"E_{epoch+1}_{file_name}")
+        torch.save(model.state_dict(), epoch_model_path)
+        print(f"[INFO] Saved Epoch {epoch+1} Model to: {epoch_model_path}")
+        
+    print(f"\n[DONE] Training Finished.")
+    print(f"  Total RL Steps Processed (cached): {total_rl_steps_loaded}")
+    print(f"  Total Gradient Steps: {global_gradient_steps}")
+        
+    print(f"\n[DONE] Training Finished.")
+    print(f"  Total RL Steps Processed: {total_rl_steps_loaded}")
+    print(f"  Total Gradient Steps: {global_gradient_steps}")
+        
+    # Save Final Model
+    torch.save(model.state_dict(), output_model)
+    print(f"[INFO] Saved Final Model to: {output_model}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Train Decision Transformer with RL Step Limit")
+    parser.add_argument("--prefix", type=str, default="trajectory_data_part_", help="Prefix of the pickle files to load")
+    parser.add_argument("--output", type=str, default="dt_model_1M_RL.pth", help="Output filename")
+    parser.add_argument("--target_rl_steps", type=int, default=1000000, help="Total RL steps (samples) to train on")
+    
+    args = parser.parse_args()
+    
+    print("\n[Configuration]")
+    print(f"  Prefix: {args.prefix}")
+    print(f"  Output: {args.output}")
+    print(f"  Target RL Steps: {args.target_rl_steps}")
+    print("-" * 30)
+    
+    train_sequential_rl_steps(data_prefix=args.prefix, output_model=args.output, target_rl_steps=args.target_rl_steps)

DT Update/OnnxModels/DT_BC_100.onnx

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DT Update/OnnxModels/DT_BC_100.pth

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DT Update/OnnxModels/E_1_DT_BC_100.onnx

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DT Update/OnnxModels/E_1_DT_BC_100.pth

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DT Update/OnnxModels/E_2_DT_BC_100.onnx

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DT Update/OnnxModels/E_3_DT_BC_100.onnx

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