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A New Approach to Training Multiple Cooperative Agents for Autonomous Driving

arXiv 2209.02157 ↗First posted 5 Sep 2022cs.AIcited by 0

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Abstract (arXiv)

Training multiple agents to perform safe and cooperative control in the complex scenarios of autonomous driving has been a challenge. For a small fleet of cars moving together, this paper proposes Lepus, a new approach to training multiple agents. Lepus adopts a pure cooperative manner for training multiple agents, featured with the shared parameters of policy networks and the shared reward function of multiple agents. In particular, Lepus pre-trains the policy networks via an adversarial process, improving its collaborative decision-making capability and further the stability of car driving. Moreover, for alleviating the problem of sparse rewards, Lepus learns an approximate reward function from expert trajectories by combining a random network and a distillation network. We conduct extensive experiments on the MADRaS simulation platform. The experimental results show that multiple agents trained by Lepus can avoid collisions as many as possible while driving simultaneously and outperform the other four methods, that is, DDPG-FDE, PSDDPG, MADDPG, and MAGAIL(DDPG) in terms of stability.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 5 Sep 2022.

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14 of 23 references.

References · 14

  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  5. 2017Counterfactual Multi-Agent Policy Gradientscited by 733
  6. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
  7. 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 401
  8. 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 224
  9. 2018Multi-Agent Imitation Learning for Driving Simulationcited by 49
  10. 2018End-to-End Race Driving with Deep Reinforcement Learningcited by 38
  11. 2020R-MADDPG for Partially Observable Environments and Limited Communicationcited by 36
  12. 2020MADRaS : Multi Agent Driving Simulatorcited by 15
2 more
  1. 2020Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Netscited by 11
  2. 2018Parameter Sharing Reinforcement Learning Architecture for Multi Agent Driving Behaviorscited by 2

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