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+norm_adv: true
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+vf_coef: 0.5
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+exp_name: ppo_antdir_v1
+seed: 88724
+torch_deterministic: true
+cuda: true
+track: true
+capture_video: true
+record_every: 100
+save_model: true
+upload_model: false
+hf_entity: ''
+task: 'AntDir-v1=>{"target_dir": [1.0, 1.0]}'
+total_timesteps: 10000
+learning_rate: 0.0003
+num_envs: 32
+num_steps: 1024
+anneal_lr: false
+gamma: 0.99
+gae_lambda: 0.95
+num_minibatches: 32
+update_epochs: 10
+norm_adv: true
+clip_coef: 0.2
+clip_vloss: true
+ent_coef: 0.005
+vf_coef: 0.5
+max_grad_norm: 0.5
+target_kl: null
+batch_size: 32768
+minibatch_size: 1024
+num_iterations: 0
+task_id: eyJ0YXJnZXRfZGlyIjogWzEuMCwgMS4wXX0
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+exp_name: ppo_antdir_v1
+seed: 9074
+torch_deterministic: true
+cuda: true
+track: true
+capture_video: true
+record_every: 50000
+save_model: true
+upload_model: false
+hf_entity: ''
+task: 'AntDir-v1=>{"target_dir": [1.0, 1.0]}'
+total_timesteps: 50000000
+learning_rate: 0.0003
+num_envs: 32
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+gamma: 0.99
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+update_epochs: 10
+norm_adv: true
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+vf_coef: 0.5
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+task_id: eyJ0YXJnZXRfZGlyIjogWzEuMCwgMS4wXX0
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diff --git a/ppo_antdir_v1/carregar_modelos.py b/ppo_antdir_v1/carregar_modelos.py
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--- /dev/null
+++ b/ppo_antdir_v1/carregar_modelos.py
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+import os
+import yaml
+import torch
+
+# O caminho para o diretório que contém todas as pastas de experimento.
+# '.' significa o diretório atual.
+diretorio_pai = '.'
+
+print(f"Procurando por pastas de experimento em: {os.path.abspath(diretorio_pai)}\n")
+
+# Lista todos os itens no diretório pai.
+# Usamos sorted() para processar em ordem alfabética, o que é mais organizado.
+for nome_pasta in sorted(os.listdir(diretorio_pai)):
+    caminho_completo_pasta = os.path.join(diretorio_pai, nome_pasta)
+
+    # Verifica se o item é realmente uma pasta (diretório)
+    if os.path.isdir(caminho_completo_pasta):
+        
+        # Define os caminhos para os arquivos de modelo e configuração
+        caminho_modelo = os.path.join(caminho_completo_pasta, 'model.cleanrl_model')
+        caminho_config = os.path.join(caminho_completo_pasta, 'config.yaml')
+
+        # Verifica se ambos os arquivos necessários existem na pasta
+        if os.path.exists(caminho_modelo) and os.path.exists(caminho_config):
+            
+            print(f"--- Processando: {nome_pasta} ---")
+            
+            # 1. Carregar os metadados do config.yaml
+            try:
+                with open(caminho_config, 'r') as f:
+                    metadados = yaml.safe_load(f)
+                print("  [✓] Metadados (config.yaml) carregados.")
+                
+                # Exemplo de como acessar um metadado específico:
+                # id_experimento = metadados.get('exp_name', 'Não definido')
+                # print(f"      - ID do Experimento: {id_experimento}")
+
+            except Exception as e:
+                print(f"  [✗] Falha ao carregar o config.yaml: {e}")
+                continue # Pula para a próxima pasta
+
+            # 2. Carregar o modelo da rede neural
+            try:
+                # Usamos map_location='cpu' para garantir que o modelo carregue
+                # mesmo que você não tenha uma GPU, ou se o modelo foi treinado em uma.
+                modelo = torch.load(caminho_modelo, map_location=torch.device('cpu'))
+                print("  [✓] Rede Neural (model.cleanrl_model) carregada.")
+
+                # A partir daqui, você pode fazer o que quiser com o modelo e os metadados
+                
+                # Exemplo: Imprimir a arquitetura do modelo
+                # print("      - Arquitetura do Modelo:")
+                # print(modelo)
+                
+                # Exemplo: Contar o número de parâmetros do modelo
+                total_params = sum(p.numel() for p in modelo.parameters())
+                print(f"      - Total de parâmetros: {total_params:,}")
+
+
+            except Exception as e:
+                print(f"  [✗] Falha ao carregar o modelo: {e}")
+
+            print("-" * (len(nome_pasta) + 20))
+            print() # Adiciona uma linha em branco para melhor visualização