mlsanityData to 7 Oct 2026

Double Deep Q-Learning in Opponent Modeling

arXiv 2211.15384 ↗First posted 24 Nov 2022cs.AIcited by 0

On the map: Mixture-of-experts → MoE in reinforcement learning and robotics

Abstract (arXiv)

Multi-agent systems in which secondary agents with conflicting agendas also alter their methods need opponent modeling. In this study, we simulate the main agent's and secondary agents' tactics using Double Deep Q-Networks (DDQN) with a prioritized experience replay mechanism. Then, under the opponent modeling setup, a Mixture-of-Experts architecture is used to identify various opponent strategy patterns. Finally, we analyze our models in two environments with several agents. The findings indicate that the Mixture-of-Experts model, which is based on opponent modeling, performs better than DDQN.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in MoE in reinforcement learning and robotics, one of 12 directions in Mixture-of-experts. Builds on 9 of its 22 references that are in the map. First posted on arXiv 24 Nov 2022.

Builds on

9 of 22 references.

References · 9

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  4. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  5. 2015Prioritized Experience Replaycited by 1,070
  6. 2017Robust Adversarial Reinforcement Learningcited by 439
  7. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 209
  8. 2016Opponent Modeling in Deep Reinforcement Learningcited by 94
  9. 2016Why Deep Neural Networks for Function Approximation?cited by 82

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