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Experience Selection in Deep Reinforcement Learning for Control

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Neural Networks: Tricks of the Trade
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Self-improving reactive agents based on reinforcement learning, planning and teaching
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Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time
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Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm
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Approximate dynamic programming with a fuzzy parameterization
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Policy derivation methods for critic-only reinforcement learning in continuous spaces
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Evaluation of physical damage associated with action selection strategies in reinforcement learning
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To recognize shapes, first learn to generate images
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On the Theory of the Brownian Motion
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Reinforcement learning in continuous time: advantage updating
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Improved deep reinforcement learning for robotics through distribution-based experience retention
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Q-learning with experience replay in a dynamic environment
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Sequential Decision Making With Coherent Risk
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Parallel Online Temporal Difference Learning for Motor Control
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Dyna, an integrated architecture for learning, planning, and reacting
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Curriculum learning
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Random sampling with a reservoir
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Learning user's preferences by analyzing Web-browsing behaviors
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A Theory of the Learnable
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Reinforcement learning in robotics: A survey
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Bootstrap Methods: Another Look at the Jackknife
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Q-PrOP: Sample-efficient policy gradient with an off-policy critic
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Language Understanding for Text-based Games using Deep Reinforcement Learning
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Intrinsically Motivated Reinforcement Learning
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AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)
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On the Number of Linear Regions of Deep Neural Networks
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Continuous control with deep reinforcement learning
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Online Batch Selection for Faster Training of Neural Networks
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Deep Exploration via Bootstrapped DQN
inferredcites →https://doi.org/10.48550/arxiv.1611.01626openalex-snapshot
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Parameter Space Noise for Exploration
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A Possibility for Implementing Curiosity and Boredom in Model-Building Neural Controllers

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Pruning filters with L1-norm and capped L1-norm for CNN compression
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DDPG-Based Decision-Making Strategy of Adaptive Cruising for Heavy Vehicles Considering Stability
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Batch Prioritization in Multigoal Reinforcement Learning
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Off-Policy Correction for Deep Deterministic Policy Gradient Algorithms via Batch Prioritized Experience Replay
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