LIF SNN DQN trained from scratch via BPTT with surrogate gradients. 100% reliable — always reaches 100% solved. The recommended approach for spiking reinforcement learning. No conversion needed — native SNN training.

Model Details

Task: reinforcement-learning
Dataset: CartPole-v1
Architecture: 3-layer MLP DQN (4→128→128→2) with LIF neurons
Training: Direct LIF SNN training from scratch (BPTT + surrogate gradient)
Status: production

Performance

Solved: 100% (solved at ~350 episodes)
Sparsity: 68.5%
Parameters: 17,922 (70 KB)
Timesteps: T=16

Usage

python

import neurocuda as nc

# Load the pre-converted spiking model
snn, info = nc.hub.load("neurocuda/lif-dqn-cartpole-snn")

# The model is already spiking — binary IF/LIF spikes, stateful membrane
snn.eval()

# 4D input (single frame)
import torch
x = torch.randn(1, 2, 34, 34)  # Adjust channels/size for your model
output = snn(x)

# 5D input (temporal — event cameras, video)
x5 = torch.randn(2, 16, 2, 34, 34)  # (Batch, Timesteps, Channels, H, W)
output5 = snn(x5)

Hardware Compatibility

Validated on: GPU, CPU
NIR Export: Not yet

Conversion Method

Direct LIF SNN training from scratch (BPTT + surrogate gradient)

Citation

bibtex

@software{neurocuda2026,
  title    = {NeuroCUDA: A PyTorch-to-Neuromorphic Compiler},
  author   = {Krishna Varma},
  year     = {2026},
  url      = {https://github.com/neurocuda/neurocuda}
}

Limitations

This is a converted spiking neural network. Accuracy was measured on the full test set with ≥3 seeds (where noted). Performance may vary on different hardware backends. See the NeuroCUDA README for detailed benchmarking methodology.

Krishnav1234/neurocuda-lif-dqn-cartpole-snn

neurocuda/lif-dqn-cartpole-snn ✅