# Overcoming the Spectral Bias of Neural Value Approximation

@article{Yang2022OvercomingTS, title={Overcoming the Spectral Bias of Neural Value Approximation}, author={Ge Yang and Anurag Ajay and Pulkit Agrawal}, journal={ArXiv}, year={2022}, volume={abs/2206.04672} }

Value approximation using deep neural networks is at the heart of off-policy deep reinforcement learning, and is often the primary module that provides learning signals to the rest of the algorithm. While multi-layer perceptron networks are universal function approximators, recent works in neural kernel regression suggest the presence of a spectral bias, where fitting high-frequency components of the value function requires exponentially more gradient update steps than the low-frequency ones…

## 7 Citations

### Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime

- Computer ScienceArXiv
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The proof exploits the low-effective-rank property of the Fisher Information Matrix at initialization, which implies a low effective dimension of the model (far smaller than the number of parameters) and concludes that local capacity control from the low effective rank of the Fischer Information Matrix is still underexplored theoretically.

### Learning Dynamics and Generalization in Reinforcement Learning

- Computer ScienceArXiv
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The learning dynamics of temporal difference algorithms are analyzed to gain novel insight into the tension between these two objectives and it is shown theoretically that temporal difference learning encourages agents to non-smooth components of the value function early in training, and at the same time induces the second-order effect of discouraging generalization.

### Neural networks trained with SGD learn distributions of increasing complexity

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### The Ladder in Chaos: A Simple and Effective Improvement to General DRL Algorithms by Policy Path Trimming and Boosting

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This paper studies how the policy networks of typical DRL agents evolve during the learning process by empirically investigating several kinds of temporal change for each policy parameter, and proposes a simple and effective method, called Policy Path Trimming and Boosting (PPTB), as a general plug-in improvement to DRL algorithms.

### Contrastive Learning as Goal-Conditioned Reinforcement Learning

- Computer ScienceArXiv
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This paper builds upon prior work and applies contrastive representation learning to action-labeled trajectories, in such a way that the (inner product of) learned representations exactly corresponds to a goal-conditioned value function.

### Strong Lensing Source Reconstruction Using Continuous Neural Fields

- PhysicsArXiv
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From the nature of dark matter to the rate of expansion of our Universe, observations of distant galaxies distorted through strong gravitational lensing have the potential to answer some of the major…

### Learning Dynamics and Generalization in Deep Reinforcement Learning

- Computer ScienceICML
- 2022

The learning dynamics of temporal difference algorithms are analyzed to gain novel insight into the tension between these two objectives, and it is shown theoretically that temporal difference learning encourages agents to fit non-smooth components of the value function early in training, and at the same time induces the second-order effect of discouraging generalization.

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