MTA-RL predicts 3D driving affordances from multi-modal sensors with a transformer and uses them as the observation space for an RL policy, yielding better route completion and generalization than baselines in CARLA urban scenarios.
End-to-end model-free reinforcement learning for urban driving using implicit affordances
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Introduces circulate-firing neurons, time-step-wise learnable surrogate gradients, and balanced loss for direct SNN training, reporting competitive results on datasets and Transformers.
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MTA-RL: Robust Urban Driving via Multi-modal Transformer-based 3D Affordances and Reinforcement Learning
MTA-RL predicts 3D driving affordances from multi-modal sensors with a transformer and uses them as the observation space for an RL policy, yielding better route completion and generalization than baselines in CARLA urban scenarios.
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Advancing Direct Training for Spiking Neural Networks with Circulate-Firing Neurons and Learnable Gradients
Introduces circulate-firing neurons, time-step-wise learnable surrogate gradients, and balanced loss for direct SNN training, reporting competitive results on datasets and Transformers.