Biologically inspired architectures for sample-efficient deep reinforcement learning
This paper discusses new methods for improving how machines learn to make decisions by using fewer resources. It draws inspiration from how nature works, aiming to make learning faster and more efficient.
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- 1 First, the low-rank properties of learned perceptual manifolds are giving rise to a rich theory borrowing from statistical physics.
- 2 Several tracks of concurrent research are being investigated, and have reduced by orders of magnitude the number of environment interactions required for good performance beyond the previous benchmark of biologically-inspired episodic control methods to a couple hours of human gameplay time .
- 3 But these breakthroughs have not, so far, been reflected as inductive priors in the shape of modifications in deep RL neural networks architectures, which remain fairly fixed on the Atari domain.
- 4 A tensor X \u2208 R I1\u00d7I2\u00d7\u2022\u2022\u2022\u00d7I N , can be decomposed into a sum of R rank-1 tensors, known as the Canonical-Polyadic decomposition, where R is as the rank of the decomposition.
Introduction
The successes of deep reinforcement learning (thereafter ‘RL’) come at a heavy computational price. It is well known that achieving human-level performance in domains such as Atari requires hundreds of millions of frames of environment interaction.
As such, the problem of sample efficiency in reinforcement learning is of critical importance.
However, while the data-efficiency of RL methods has seen recent drastic performance, their function approximators still use millions of learned weights, potentially still leaving them heavily overparameterized.
Methodology
A tensor regression layer estimates the regression weight tensor W \u2208 R I1\u00d7I2\u00d7\u2022\u2022\u2022\u00d7I N under a low-rank decomposition. We do take as a baseline method the data-efficient Rainbow of .
Study Design
\u2022 We replace the fully-connected, linear layers used in the Rainbow and data-efficient Rainbow by tensor regression layers in order to learn low-rank policies (ranks in appendix). \u2022 We use either the K-FAC second order stochastic optimizer, or ADAM .
The corresponding tensor regression layer ranks are in appendix, and chosen to target 400k, 200k and 100k coefficients respectively.
Results & Findings
Several tracks of concurrent research are being investigated, and have reduced by orders of magnitude the number of environment interactions required for good performance beyond the previous benchmark of biologically-inspired episodic control methods to a couple hours of human gameplay time . Very recent work from studies the effect of inductive bias of neural architectures in reinforcement learning ; they forego training altogether, but transfer networks that only obtain ‘better.
- Several tracks of concurrent research are being investigated, and have reduced by orders of magnitude the number of environment interactions required for good performance beyond the.
- Very recent work from studies the effect of inductive bias of neural architectures in reinforcement learning ; they forego training altogether, but transfer networks that only.
- In similar fashion, investigate the effect of random projections in the restricted setting of imitation learning.
- First, the low-rank properties of learned perceptual manifolds are giving rise to a rich theory borrowing from statistical physics.
- But these breakthroughs have not, so far, been reflected as inductive priors in the shape of modifications in deep RL neural networks architectures, which remain fairly.
First, the low-rank properties of learned perceptual manifolds are giving rise to a rich theory borrowing from statistical physics.
Several tracks of concurrent research are being investigated, and have reduced by orders of magnitude the number of environment interactions required for good performance beyond the previous benchmark of biologically-inspired episodic control methods to a couple hours of human gameplay.
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Practical Applications
Smaller networks not only train faster, but may yet offer another avenue for gains in the form of better generalization . Concurrently, the study of biologically-inspired models of learning has exhibited two mathematical characterizations that might be critical in explaining how biological learning takes place so efficiently.
Deep Reinforcement Learning
This section outlines the Markov Decision Process framework used in reinforcement learning, detailing the components such as states, actions, rewards, and policies. It explains the Q-learning approach and various refinements like Double Q-learning and prioritized RL, emphasizing the importance of efficient learning mechanisms.
Tensor Factorization
The section introduces tensor factorization concepts, specifically CP and Tucker decompositions, which serve as multilinear algebra analogues to traditional matrix decompositions. It explains how these decompositions can be applied to create low-rank approximations for neural network layers.
Tensor Regression Layer
This section defines a tensor regression layer that utilizes tensor factorization for estimating regression weights under low-rank constraints. It describes the mathematical formulation and how it integrates into the learning process of deep networks.
Frequently Asked Questions
Second, another well known line of work has identified Gabor filters (and more generally wavelet filter-like structures) in the actual visual cortex of animals , and linked those to sparsity-promoting methods and dictionary learning . To the best of our knowledge, this.
A tensor regression layer estimates the regression weight tensor W \u2208 R I1\u00d7I2\u00d7\u2022\u2022\u2022\u00d7I N under a low-rank decomposition. The corresponding tensor regression layer ranks are in appendix, and chosen to target 400k, 200k and 100k coefficients respectively.
Several tracks of concurrent research are being investigated, and have reduced by orders of magnitude the number of environment interactions required for good performance beyond the previous benchmark of biologically-inspired episodic control methods to a couple hours of human gameplay time .
Smaller networks not only train faster, but may yet offer another avenue for gains in the form of better generalization . Concurrently, the study of biologically-inspired models of learning has exhibited two mathematical characterizations that might be critical in explaining how biological.
This paper discusses new methods for improving how machines learn to make decisions by using fewer resources. It draws inspiration from how nature works, aiming to make learning faster and more efficient.
Yes. PDFDigest can turn this paper into a structured explanation, key takeaways, visual summaries, and a narrated video when available.