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Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization

arXiv 1905.04100 ↗First posted 19 Feb 2019cs.NE cs.ROcited by 23

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Abstract (arXiv)

Reinforcement learning (RL) enables agents to take decision based on a reward function. However, in the process of learning, the choice of values for learning algorithm parameters can significantly impact the overall learning process. In this paper, we use a genetic algorithm (GA) to find the values of parameters used in Deep Deterministic Policy Gradient (DDPG) combined with Hindsight Experience Replay (HER), to help speed up the learning agent. We used this method on fetch-reach, slide, push, pick and place, and door opening in robotic manipulation tasks. Our experimental evaluation shows that our method leads to better performance, faster than the original algorithm.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Feb 2019.

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Citing papers per year: 2021: 4; 2022: 9; 2023: 5; 2024: 3; 2025: 2.

23 papers.

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  1. 2021Symbolic Regression via Neural-Guided Genetic Programming Population Seedingcited by 97
  2. 2022Automated Reinforcement Learning (AutoRL): A Survey and Open Problemscited by 69
  3. 2023PopulAtion Parameter Averaging (PAPA)cited by 23
  4. 2022Combining Evolution and Deep Reinforcement Learning for Policy Search: a Surveycited by 20
  5. 2023Rapid Motor Adaptation for Robotic Manipulator Armscited by 13
  6. 2022Automated Reinforcement Learning: An Overviewcited by 9
  7. 2022New Challenges in Reinforcement Learning: A Survey of Security and Privacycited by 7
  8. 2023Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfindingcited by 5
  9. 2022Innovations in Integrating Machine Learning and Agent-Based Modeling of Biomedical Systemscited by 4
  10. 2021Optimal Actor-Critic Policy with Optimized Training Datasetscited by 2
  11. 2022ARLO: A Framework for Automated Reinforcement Learningcited by 2
  12. 2022Balancing Profit, Risk, and Sustainability for Portfolio Managementcited by 2
11 more
  1. 2025Probabilistic Human Intent Prediction for Mobile Manipulation: An Evaluation with Human-Inspired Constraintscited by 2
  2. 2022Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammogramscited by 1
  3. 2022AACHER: Assorted Actor-Critic Deep Reinforcement Learning with Hindsight Experience Replaycited by 1
  4. 2023A Semantic Modular Framework for Events Topic Modeling in Social Mediacited by 1
  5. 2024Deep Dive into Model-free Reinforcement Learning for Biological and Robotic Systems: Theory and Practicecited by 1
  6. 2024Human-Readable Programs as Actors of Reinforcement Learning Agents Using Critic-Moderated Evolutioncited by 1
  7. 2024Pulling the Carpet Below the Learner's Feet: Genetic Algorithm To Learn Ensemble Machine Learning Model During Concept Driftcited by 1
  8. 2021Advances in Electron Microscopy with Deep Learningcited by 0
  9. 2021Quadratic speedup of global search using a biased crossover of two good solutionscited by 0
  10. 2023Stepsize Learning for Policy Gradient Methods in Contextual Markov Decision Processescited by 0
  11. 2025Dual-Individual Genetic Algorithm: A Dual-Individual Approach for Efficient Training of Multi-Layer Neural Networkscited by 0