Back to Search
Start Over
Multi-Agent Transfer Reinforcement Learning With Multi-View Encoder for Adaptive Traffic Signal Control
- Source :
- IEEE Transactions on Intelligent Transportation Systems. 23:12572-12587
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Multi-agent reinforcement learning (MARL) based methods for adaptive traffic signal control (ATSC) have shown promising potentials to solve the heavy traffic problems. The existing MARL methods adopt centralized or distributed strategies. The former only models the environment as an agent and suffers from the exponential growth of action and state space. The latter extends the independent reinforcement learning methods, such as DQN, to multiple interactions directly or propagates information, such as state and policy, without taking their qualities into account. In this paper, we propose a multi-agent transfer reinforcement learning method to enhance the performance of MARL for ATSC, which is termed as multi-agent transfer soft actor-critic with the multi-view encoder (MT-SAC). The MT-SAC combines centralized and distributed strategies. In MT-SAC, we propose a multi-view state encoder and a transfer learning paradigm with guidance. The encoder processes input states from multiple perspectives and uses an attention mechanism to weigh the neighborhood information. While the paradigm enables the agents to handle different conditions for improving generalization abilities by transfer learning. Experimental studies on different scale road networks show that the MT-SAC outperforms the state-of-the-art algorithms and makes the traffic signal controllers more collaborative and robust.
- Subjects :
- business.industry
Generalization
Computer science
Mechanical Engineering
Control (management)
Scale (descriptive set theory)
Computer Science Applications
Automotive Engineering
Reinforcement learning
State space
State (computer science)
Artificial intelligence
Transfer of learning
business
Encoder
Subjects
Details
- ISSN :
- 15580016 and 15249050
- Volume :
- 23
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Intelligent Transportation Systems
- Accession number :
- edsair.doi...........f345b7c61fca2abc9734f316844692e7