Optimizing Sensory Neurons: Nonlinear Attention Mechanisms for Accelerated Convergence in Permutation-Invariant Neural Networks for Reinforcement Learning
This work addresses training efficiency for reinforcement learning practitioners, but it is incremental as it builds on existing permutation-invariant architectures.
The paper tackled the problem of high computational cost and slow training in reinforcement learning by proposing a nonlinear attention mechanism that enhances feature interactions, resulting in significantly faster convergence while maintaining baseline performance.
Training reinforcement learning (RL) agents often requires significant computational resources and prolonged training durations. To address this challenge, we build upon prior work that introduced a neural architecture with permutation-invariant sensory processing. We propose a modified attention mechanism that applies a non-linear transformation to the key vectors (K), producing enriched representations (K') through a custom mapping function. This Nonlinear Attention (NLA) mechanism enhances the representational capacity of the attention layer, enabling the agent to learn more expressive feature interactions. As a result, our model achieves significantly faster convergence and improved training efficiency, while maintaining performance on par with the baseline. These results highlight the potential of nonlinear attention mechanisms to accelerate reinforcement learning without sacrificing effectiveness.