mlsanityData to 14 Oct 2026

Evolutionary Policy Optimization

arXiv 2504.12568 ↗First posted 17 Apr 2025cs.LG cs.NEcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

A key challenge in reinforcement learning (RL) is managing the exploration-exploitation trade-off without sacrificing sample efficiency. Policy gradient (PG) methods excel in exploitation through fine-grained, gradient-based optimization but often struggle with exploration due to their focus on local search. In contrast, evolutionary computation (EC) methods excel in global exploration, but lack mechanisms for exploitation. To address these limitations, this paper proposes Evolutionary Policy Optimization (EPO), a hybrid algorithm that integrates neuroevolution with policy gradient methods for policy optimization. EPO leverages the exploration capabilities of EC and the exploitation strengths of PG, offering an efficient solution to the exploration-exploitation dilemma in RL. EPO is evaluated on the Atari Pong and Breakout benchmarks. Experimental results show that EPO improves both policy quality and sample efficiency compared to standard PG and EC methods, making it effective for tasks that require both exploration and local optimization.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 23 references that are in the map. First posted on arXiv 17 Apr 2025.

Builds on

10 of 23 references.

References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2015Trust Region Policy Optimizationcited by 2,580
  5. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  6. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 574
  7. 2016Sample Efficient Actor-Critic with Experience Replaycited by 188
  8. 2017Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learningcited by 150
  9. 2017Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agentscited by 107
  10. 2017Evolving Deep Neural Networkscited by 95

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  1. 2025Evolutionary Policy Optimization
  2. 2024Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms
  3. 2024Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy Networks
  4. 2024Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning
  5. 2024Ancestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning
  6. 2026Proximal Policy Optimization with Evolutionary Mutations
  7. 2024The Exploration-Exploitation Dilemma Revisited: An Entropy Perspective
  8. 2025Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization