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Combining Evolution and Deep Reinforcement Learning for Policy Search: A Survey

https://doi.org/10.1145/3569096
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What this work says

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

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Gradient Bias to Solve the Generalization Limit of Genetic Algorithms Through Hybridization with Reinforcement Learning
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An Adaptive Stochastic Gradient-Free Approach for High-Dimensional Blackbox Optimization
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Q-learning
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Derivative-free reinforcement learning: a review
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COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS11Research reported in this paper was supported in part by the National Science Foundation under grant DCR 71-01997 and by the Horace H. Rackham School of Graduate Studies under grant 387156.
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Recruitment-imitation mechanism for evolutionary reinforcement learning
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Reinforcement learning versus evolutionary computation: A survey on hybrid algorithms
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The Cross-Entropy Method for Combinatorial and Continuous Optimization
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Human-level control through deep reinforcement learning
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Embodied intelligence via learning and evolution
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Evolutionary computation: comments on the history and current state
inferredcites →https://doi.org/10.1109/access.2020.3008735openalex-snapshot
Efficient Novelty Search Through Deep Reinforcement Learning
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GA+DDPG+HER: Genetic Algorithm-Based Function Optimizer in Deep Reinforcement Learning for Robotic Manipulation Tasks
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Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94)
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Quality and Diversity Optimization: A Unifying Modular Framework
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Reinforcement Learning: An Introduction
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THE BALDWIN EFFECT
inferredcites →https://doi.org/10.1126/science.aau6249openalex-snapshot
Human-level performance in 3D multiplayer games with population-based reinforcement learning
inferredcites →https://doi.org/10.1145/1102351openalex-snapshot
Proceedings of the 22nd international conference on Machine learning - ICML '05
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Cartesian genetic programming
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Instinct-driven dynamic hardware reconfiguration
inferredcites →https://doi.org/10.1145/3205455.3205473openalex-snapshot
Safe mutations for deep and recurrent neural networks through output gradients
inferredcites →https://doi.org/10.1145/3321707.3321752openalex-snapshot
Novelty search
inferredcites →https://doi.org/10.1145/3449639.3459304openalex-snapshot
Policy gradient assisted MAP-Elites
inferredcites →https://doi.org/10.1145/3449726.3459475openalex-snapshot
Evo-RL
inferredcites →https://doi.org/10.1145/3449726.3463171openalex-snapshot
Behavior-based neuroevolutionary training in reinforcement learning
inferredcites →https://doi.org/10.1145/3512290.3528705openalex-snapshot
Approximating gradients for differentiable quality diversity in reinforcement learning
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Diversity Evolutionary Policy Deep Reinforcement Learning
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Reinforcement Learning for Improving Agent Design
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An Analysis of Generalization in the XCS Classifier System
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Abandoning Objectives: Evolution Through the Search for Novelty Alone
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Trust Region Evolution Strategies
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Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
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Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari
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Self-Guided Evolution Strategies with Historical Estimated Gradients
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Quality Diversity: A New Frontier for Evolutionary Computation
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Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning
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Proceedings of the 29th International Conference on Machine Learning (ICML-12)
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Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
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Natural evolution strategies
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Evolution and Learning

What points at this work

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

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Evolutionary Action Selection for Gradient-Based Policy Learning
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Genetic-Enhanced Cross-Entropy reinforcement learning
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PriMaχ: Novel Genetic Reinforcement Learning Model for Improving Privacy Preservation in Blockchain
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