test_model.py

import torch
import gymnasium as gym
import torch.nn as nn

class Actor(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_size=64):
        super().__init__()
        self.network = nn.Sequential(
            nn.Linear(state_dim, hidden_size),
            nn.Tanh(),
            nn.Linear(hidden_size, hidden_size),
            nn.Tanh(),
            nn.Linear(hidden_size, action_dim)
        )
    
    def forward(self, x):
        return self.network(x)

def test_model():
    # Load the model
    checkpoint = torch.load("model.pt", map_location='cpu')
    actor = Actor(state_dim=8, action_dim=4, hidden_size=checkpoint['config']['hidden_size'])
    actor.load_state_dict(checkpoint['actor_state_dict'])
    actor.eval()
    
    # Test the agent
    env = gym.make("LunarLander-v2", render_mode="human")
    state, _ = env.reset()
    total_reward = 0
    
    for _ in range(1000):
        with torch.no_grad():
            state_tensor = torch.FloatTensor(state).unsqueeze(0)
            logits = actor(state_tensor)
            action = torch.argmax(logits, dim=-1).item()
        
        state, reward, terminated, truncated, _ = env.step(action)
        total_reward += reward
        
        if terminated or truncated:
            break
    
    env.close()
    print(f"Total reward: {total_reward:.2f}")

if __name__ == "__main__":
    test_model()