Placing trainable nonlinear functions on connections in analogue networks enables efficient representation of smooth continuous targets with hardware transfer at projected 30 microwatt power.
Barto, Richard S
5 Pith papers cite this work, alongside 106 external citations. Polarity classification is still indexing.
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Pith papers citing it
106
external citations · external index
representative citing papers
Proposes Prior Random Testing (PRT) that leverages task difficulty to prioritize failure-prone test cases for DRL agents, achieving over 50% lower testing cost than random testing while preserving diversity on four benchmarks.
QnRL is a distributional quantum RL framework that distills conditional action policies from moments of quantum generative models in Hilbert space via the QuAK algorithm, reporting higher scores and fewer parameters than baselines.
The DeepMind Control Suite supplies a standardized collection of continuous control tasks with interpretable rewards for benchmarking reinforcement learning agents.
Exact critic in entropy-regularized actor-critic yields strong variance reduction, enabling Õ(log(1/ε)) sample complexity for ε-optimal regularized value.
citing papers explorer
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Low-power analogue neural networks with trainable nonlinear connections for continuous control
Placing trainable nonlinear functions on connections in analogue networks enables efficient representation of smooth continuous targets with hardware transfer at projected 30 microwatt power.
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Failure-Based Testing for Deep Reinforcement Learning Agents
Proposes Prior Random Testing (PRT) that leverages task difficulty to prioritize failure-prone test cases for DRL agents, achieving over 50% lower testing cost than random testing while preserving diversity on four benchmarks.
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QnRL: Quantum-Native Reinforcement Learning
QnRL is a distributional quantum RL framework that distills conditional action policies from moments of quantum generative models in Hilbert space via the QuAK algorithm, reporting higher scores and fewer parameters than baselines.
-
DeepMind Control Suite
The DeepMind Control Suite supplies a standardized collection of continuous control tasks with interpretable rewards for benchmarking reinforcement learning agents.
-
Refined Analysis of Entropy-Regularized Actor-Critic
Exact critic in entropy-regularized actor-critic yields strong variance reduction, enabling Õ(log(1/ε)) sample complexity for ε-optimal regularized value.