Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Distributed Approach for Coordination of Traffic Signal Agents
Autonomous Agents and Multi-Agent Systems
Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool METANET
IEEE Transactions on Intelligent Transportation Systems
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A distributed approach to Reinforcement Learning in tasks of ramp metering and dynamic route guidance is presented. The problem domain, a freeway integration control application, is formulated as a distributed reinforcement learning problem. The DRL approach was implemented via a multi-agent control architecture where the decision agent was assigned to each of the on-ramp or VMS. The return of each agent is simultaneously updating a single shared policy. The control strategy’s efficiency is demonstrated through its application to the simple freeway network. Analyses of simulation results using this approach show significant improvement over traditional local control, especially for the case of large traffic demand. Using the DRL approach, the TTS of the Network has been reduced by 20% under the heavy demands.