Update README.md

Author: Vishal-sys-code

README.md

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-# Spiking Decision Transformer
+<div align="center">
 
+# Neuromorphic Decision Transformer (SNN-DT)
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![Python](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
+[![PyTorch](https://img.shields.io/badge/PyTorch-1.12+-ee4c2c.svg)](https://pytorch.org/)
+[![arXiv](https://img.shields.io/badge/arXiv-2508.21505-b31b1b.svg)](https://arxiv.org/abs/2508.21505)
+
+**Local Plasticity, Phase-Coding, and Dendritic Routing for Low-Power Sequence Control**
+
+</div>
+
+## Abstract
+
+This repository contains the official PyTorch implementation of the **Spiking Decision Transformer (SNN-DT)**, as presented in the paper *"Spiking Decision Transformers: Local Plasticity, Phase-Coding, and Dendritic Routing for Low-Power Sequence Control"* (Pandey & Biswas, 2025).
+
+The SNN-DT architecture bridges the gap between the sequential modeling capabilities of Transformers and the energy efficiency of Spiking Neural Networks (SNNs). By embedding **Leaky Integrate-and-Fire (LIF)** neurons within the self-attention mechanism and utilizing accurate **STDP-inspired local plasticity**, this model achieves state-of-the-art performance on continuous control tasks while reducing energy consumption by orders of magnitude compared to traditional ANN-based Decision Transformers.
+
+<div align="center">
+  <img src="visualizations/model_architecture.png" alt="SNN-DT Architecture" width="800">
+</div>
+*(Note: Visualizations available in the `visualizations/` directory)*
+
+## Key Features
+
+-   **Neuromorphic Efficiency**: Replaces standard activation functions with temporal spike-based logic, significantly reducing computational overhead suitable for edge deployment.
+-   **Phase-Coded Positional Encoding**: A biologically plausible method for encoding sequence order using spike timing phases.
+-   **Dendritic Routing**: Efficient information routing mechanism mimicking biological dendritic trees.
+-   **Three-Factor Local Plasticity**: Implements STDP-like learning rules for robust weight updates without heavy backpropagation costs during inference-time adaptation.
+-   **Standard Gym Benchmarks**: Evaluated on classic control tasks: `CartPole-v1`, `Pendulum-v1`, `MountainCar-v0`, and `Acrobot-v1`.
+
+## Installation
+
+System requirements: Linux/Windows, Python 3.8+, CUDA-enabled GPU (recommended).
+
+```bash
+# Clone the repository
+git clone https://github.com/Vishal-sys-code/neuromorphic_decision_transformer.git
+cd neuromorphic_decision_transformer
+
+# Create a virtual environment (optional but recommended)
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+## Usage
+
+### Training
+To train the SNN-DT model on a specific environment (e.g., `Pendulum-v1`), use the provided training script. The training pipeline handles data generation, preprocessing, and model optimization.
+
+```bash
+# Run training for Pendulum-v1
+python snn-dt/scripts/train.py --model snn_dt --env "Pendulum-v1" --save-dir "results/snn_dt_pendulum"
+```
+
+To run the full suite of experiments across all environments:
+```bash
+./run_all_experiments.sh
+```
+
+### Evaluation
+Evaluate a pre-trained checkpoint to measure return, spike counts, latency, and estimated energy consumption.
+
+```bash
+python eval_snn_dt.py \
+    --env "Pendulum-v1" \
+    --checkpoint_path "results/snn_dt_pendulum/best_model.pt" \
+    --target_return -200 \
+    --episodes 50
+```
+
+**Key Arguments:**
+-   `--context_len`: Context length ($K$) for the transformer (default: 20).
+-   `--per_spike_energy`: Estimated energy per spike in Joules (default: 4.6pJ for 45nm process).
+
+## Project Structure
+
+```
+neuromorphic_decision_transformer/
+├── configs/            # YAML configuration files for experiments
+├── snn-dt/             # Core SNN-DT source code
+│   ├── src/            # Model definitions and extensive util libraries
+│   └── scripts/        # Training and utility scripts
+├── demos/              # Demonstration notebooks and videos
+├── eval_snn_dt.py      # Standalone evaluation script
+├── requirements.txt    # Python dependencies
+└── run_all_experiments.sh # Batch experiment runner
+```
+
+## Citation
+
+If you use this code or find our work helpful, please cite our paper:
+
+```bibtex
+@article{pandey2025spiking,
+  title={Spiking Decision Transformers: Local Plasticity, Phase-Coding, and Dendritic Routing for Low-Power Sequence Control},
+  author={Pandey, Vishal and Biswas, Debasmita},
+  journal={arXiv preprint arXiv:2508.21505},
+  year={2025}
+}
+```
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.