⚡ Optimize tensor creation in agent.py

[google-labs-jules]

This PR optimizes the tensor creation process in the DDQNAgent.optimize_model method.

💡 What:

• Replaced the inefficient pattern torch.tensor(data).to(device) with torch.as_tensor(data, device=device).
• Applied this to: action_batch, reward_batch, done_batch, weights_batch, gamma_batch.
• Applied this to state unpacking (matrices and sectors) for both Hybrid and Legacy architectures.
• Fixed a broken import path in tests/test_model.py (gen2.model -> model) to enable proper verification.

🎯 Why:

• The previous method created a tensor on the CPU first, then copied it to the target device. This incurs double allocation and unnecessary data movement.
• torch.as_tensor with the device argument allows creating the tensor directly on the target device (or copying directly to it), skipping the intermediate CPU tensor object overhead.

📊 Measured Improvement:

• Benchmark: A focused micro-benchmark (benchmark_tensor_creation.py) simulating batch creation (batch_size=64).
• Baseline: ~0.72s (10000 iterations)
• Optimized: ~0.57s (10000 iterations)
• Speedup: ~1.25x reduction in tensor creation overhead.
• Note: This was measured on CPU (sandbox environment). On a real GPU system, the performance gain should be even more significant by avoiding PCIe transfer overhead for the intermediate CPU tensor.

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PR created automatically by Jules for task 11727680076001158903 started by @dzaczek

[google-labs-jules]

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