[Bugfix] Fix CUDA/CPU mismatch in threaded training

[bmind7]

Proposed change(s)

On Windows, running with threaded: true produced “tensors on different devices” errors. Threaded trainers create tensors in multiple threads. Implicit CPU allocations (or per-thread default device changes) led to CPU-CUDA mixing and PyTorch mode stack corruption. Making device placement explicit and consistent prevents both classes of errors.

• policy/torch_policy.py
  - Create action masks, observations, and RNN memories on default_device() during inference.
• torch_entities/utils.py
  - ModelUtils.list_to_tensor() and list_to_tensor_list() now allocate on default_device().
• torch_entities/networks.py
  - VectorInput.update_normalization() now uses device-correct tensors.
• optimizer/torch_optimizer.py, poca/optimizer_torch.py
  - Initialize zero RNN memories on default_device().
• torch_entities/components/reward_providers/gail_reward_provider.py
  - Ensure DONE tensors, epsilons, and accumulators allocate on the correct device.

Useful links (Github issues, JIRA tickets, ML-Agents forum threads etc.)

• https://discussions.unity.com/t/ml-agents-4-0-0-is-now-available/1681770/5

Types of change(s)

• [x ] Bug fix

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✅ maryamziaa
❌ bmind7
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[Copilot]

Pull Request Overview

This PR fixes CUDA/CPU device mismatch errors that occur during threaded training on Windows by making tensor device placement explicit and consistent across the codebase.

• Ensures all tensor creation operations use default_device() to maintain device consistency
• Fixes issues where tensors were implicitly created on different devices in multi-threaded environments
• Updates utilities, networks, optimizers, and reward providers to use explicit device placement

Reviewed Changes

Copilot reviewed 6 out of 6 changed files in this pull request and generated no comments.

Show a summary per file

File	Description
torch_entities/utils.py	Updates tensor creation utilities to allocate on default device
torch_entities/networks.py	Fixes device placement in vector input normalization
torch_entities/components/reward_providers/gail_reward_provider.py	Ensures GAIL reward provider tensors use correct device
policy/torch_policy.py	Makes device placement explicit for masks, observations, and RNN memories
poca/optimizer_torch.py	Initializes zero RNN memories on default device
optimizer/torch_optimizer.py	Fixes RNN memory initialization device placement

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[maryamziaa]

Hi, Please fix the issue with black reformatting and then you should be good to merge the PR. Thanks!