arXiv:2406.09079v5 Announce Type: replace Abstract: Deep reinforcement learning agents progressively lose representational capacity during training: neurons become dormant, removing active capacity from the network, and effective rank collapses, leaving surviving neurons redundant. Existing remedies such as periodic resets, and special neural network architectures, are largely algorithm- or domain-specific. We propose a simple architectural fix, the Hadamard Representation (HR), which replaces a standard hidden layer with the element-wise product of two independently parameterized layers. HR o
The paper addresses a known limitation in deep reinforcement learning (RL) related to representational capacity loss, an ongoing challenge in scaling and stabilizing AI models.
Improving the representational capacity and stability of RL agents can lead to more robust and powerful AI, impacting sectors from robotics to autonomous systems.
This architectural fix offers a generalizable method for enhancing RL performance, potentially sidestepping domain-specific remedies and accelerating AI development.
- · AI researchers
- · Reinforcement learning practitioners
- · AI-powered robotics companies
- · Autonomous systems developers
- · Developers of algorithm- or domain-specific RL fixes
The Hadamard Representation could become a standard architectural component in deep reinforcement learning, improving model efficiency and reliability.
More stable and capable RL agents could accelerate the development and deployment of complex AI systems, such as advanced AI agents or real-world robotics.
Enhanced AI capabilities could put further strain on compute resources, indirectly impacting the compute supply chain and energy demands for AI training.
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Read at arXiv cs.LG